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text-generation	transformers	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) saiga_llama3_8b - bnb 4bits - Model creator: https://huggingface.co/IlyaGusev/ - Original model: https://huggingface.co/IlyaGusev/saiga_llama3_8b/ Original model description: --- language: - ru datasets: - IlyaGusev/saiga_scored license: other license_name: llama3 license_link: https://llama.meta.com/llama3/license/ --- # Saiga/Llama3 8B, Russian Llama-3-based chatbot Based on [Llama-3 8B Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct). Llama.cpp version: [link](https://huggingface.co/IlyaGusev/saiga_llama3_8b_gguf) ОСТОРОЖНО! WARNING! LET OP! I've changed the prompt format from ChatML to the original Llama-3 format in v4. Don't forget to switch formats! v4: LLama-3 prompt format: ``` <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> Ты — Сайга, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им.<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> Как дела?<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> Отлично, а у тебя?<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> Шикарно. Как пройти в библиотеку?<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> ``` v2, v3: ChatML prompt format: ``` <\|im_start\|>system Ты — Сайга, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им.<\|im_end\|> <\|im_start\|>user Как дела?<\|im_end\|> <\|im_start\|>assistant Отлично, а у тебя?<\|im_end\|> <\|im_start\|>user Шикарно. Как пройти в библиотеку?<\|im_end\|> <\|im_start\|>assistant ``` Examples: ``` User: Почему трава зеленая? Saiga: Зеленый цвет травы обусловлен наличием хлорофилла, пигмента, который поглощает свет и передает энергию для фотосинтеза. Хлорофилл содержится в клетках листьев растений и играет ключевую роль в процессе фотосинтеза, когда растения используют солнечный свет для превращения воды и углекислого газа в органические вещества, такие как глюкоза и олигоэфиры. Хлорофилл состоит из двух основных форм: хлорофилла а и хлорофилла б. Хлорофилл а поглощает свет с длиной волны около 430 нанометров (нм), что соответствует синему или голубому свету, а хлорофилл б поглощает свет с длиной волны около 660 нм, что соответствует красному свету. Когда свет поглощается хлорофиллом, он передается в молекулы воды и углекислого газа, которые используются для фотосинтеза. Таким образом, хлорофилл является основным компонентом, ответственным за зеленый цвет листьев растений, так как он поглощает свет и передает его энергию для фотосинтеза. ``` ``` User: Сочини длинный рассказ, обязательно упоминая следующие объекты. Дано: Таня, мяч Saiga: Однажды в солнечную погоду на поле для игры в футбол приехала группа друзей, чтобы провести время вместе. В числе их был и Таня, которая была страстной футбольной фанаткой и всегда радовал своих друзей своим энтузиазмом и умением забивать мячи. В этот день, как обычно, друзья решили провести соревнование между собой, чтобы определить кто из них является лучшим футболистом. Таня была очень уверена в своих силах и готовилась к этому матчу с особой серьёзностью. Когда все друзья собрались на поле, они увидели, что перед ними стоял огромный мяч, который должен был стать предметом состязания. Мяч был огромным и тяжелым, и его размеры были необычайно большими по сравнению с обычными мячами, которые используются в футболе. Таня была первая, кто решил начать игру. Она подошла к мячу и начала его удерживать, стараясь выдержать его вес и силу. Но мяч оказался настолько тяжелым, что Таня не смогла удержать его и он упал на землю. Друзья посмеялись над ее неудачей, но Таня не отчаивалась и продолжила пытаться удержать мяч. Она стала использовать все свои силы и умения, чтобы выдержать его вес и силу. Наконец, после долгих усилий, она смогла удержать мяч и начала его бросать в сторону. Мяч летел высоко вверх, и друзья смотрели, как он пролетает над полем. Но мяч неожиданно повернул и стал лететь обратно к Тане. Она успела поймать его и продолжила играть, используя все свои навыки и умения. ``` v4: - [1cc945d4ca2c7901cf989e7edaac52ab24f1a7dd](https://huggingface.co/IlyaGusev/saiga_llama3_8b/commit/1cc945d4ca2c7901cf989e7edaac52ab24f1a7dd) - dataset: [saiga_scored](https://huggingface.co/datasets/IlyaGusev/saiga_scored), scores >= 8, c66032920556c0f21bbbed05e7e04433ec954c3d - wandb [link](https://wandb.ai/ilyagusev/rulm_self_instruct/runs/dcbs9ttt) v3: - [c588356cd60bdee54d52c2dd5a2445acca8aa5c3](https://huggingface.co/IlyaGusev/saiga_llama3_8b/commit/c588356cd60bdee54d52c2dd5a2445acca8aa5c3) - dataset: [saiga_scored](https://huggingface.co/datasets/IlyaGusev/saiga_scored), scores >= 8, d51cf8060bdc90023da8cf1c3f113f9193d6569b - wandb [link](https://wandb.ai/ilyagusev/rulm_self_instruct/runs/ltoqdsal) v2: - [ae61b4f9b34fac9856d361ea78c66284a00e4f0b](https://huggingface.co/IlyaGusev/saiga_llama3_8b/commit/ae61b4f9b34fac9856d361ea78c66284a00e4f0b) - dataset code revision d0d123dd221e10bb2a3383bcb1c6e4efe1b4a28a - wandb [link](https://wandb.ai/ilyagusev/huggingface/runs/r6u5juyk) - 5 datasets: ru_turbo_saiga, ru_sharegpt_cleaned, oasst1_ru_main_branch, gpt_roleplay_realm, ru_instruct_gpt4 - Datasets merging script: [create_short_chat_set.py](https://github.com/IlyaGusev/rulm/blob/d0d123dd221e10bb2a3383bcb1c6e4efe1b4a28a/self_instruct/src/data_processing/create_short_chat_set.py) # Evaluation * Dataset: https://github.com/IlyaGusev/rulm/blob/master/self_instruct/data/tasks.jsonl * Framework: https://github.com/tatsu-lab/alpaca_eval * Evaluator: alpaca_eval_cot_gpt4_turbo_fn \| model \| length_controlled_winrate \| win_rate \| standard_error \| avg_length \| \|-----\|-----\|-----\|-----\|-----\| \|chatgpt_4_turbo \| 76.04 \| 90.00 \|1.46 \| 1270 \| \|chatgpt_3_5_turbo \| 50.00 \| 50.00 \| 0.00 \| 536 \| \|saiga_llama3_8b, v4 \| 43.64 \| 65.90 \| 2.31 \| 1200 \| \|saiga_llama3_8b, v3 \| 36.97 \| 61.08 \| 2.38 \| 1162 \| \|saiga_llama3_8b, v2 \| 33.07 \| 48.19 \| 2.45 \| 1166 \| \|saiga_mistral_7b \| 23.38 \| 35.99 \| 2.34 \| 949 \|	{}	RichardErkhov/IlyaGusev_-_saiga_llama3_8b-4bits	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]	null	2024-04-26T19:07:43+00:00	[]	[]	TAGS #transformers #safetensors #llama #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us	Quantization made by Richard Erkhov. Github Discord Request more models saiga\_llama3\_8b - bnb 4bits * Model creator: URL * Original model: URL Original model description: --------------------------- language: * ru datasets: * IlyaGusev/saiga\_scored license: other license\_name: llama3 license\_link: URL --- Saiga/Llama3 8B, Russian Llama-3-based chatbot ============================================== Based on Llama-3 8B Instruct. URL version: link ОСТОРОЖНО! WARNING! LET OP! I've changed the prompt format from ChatML to the original Llama-3 format in v4. Don't forget to switch formats! v4: LLama-3 prompt format: v2, v3: ChatML prompt format: Examples: v4: * 1cc945d4ca2c7901cf989e7edaac52ab24f1a7dd * dataset: saiga\_scored, scores >= 8, c66032920556c0f21bbbed05e7e04433ec954c3d * wandb link v3: * c588356cd60bdee54d52c2dd5a2445acca8aa5c3 * dataset: saiga\_scored, scores >= 8, d51cf8060bdc90023da8cf1c3f113f9193d6569b * wandb link v2: * ae61b4f9b34fac9856d361ea78c66284a00e4f0b * dataset code revision d0d123dd221e10bb2a3383bcb1c6e4efe1b4a28a * wandb link * 5 datasets: ru\_turbo\_saiga, ru\_sharegpt\_cleaned, oasst1\_ru\_main\_branch, gpt\_roleplay\_realm, ru\_instruct\_gpt4 * Datasets merging script: create\_short\_chat\_set.py Evaluation ========== * Dataset: URL * Framework: URL * Evaluator: alpaca\_eval\_cot\_gpt4\_turbo\_fn	[]	[ "TAGS\n#transformers #safetensors #llama #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n" ]
text-generation	transformers	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) saiga_gemma_9b - bnb 4bits - Model creator: https://huggingface.co/IlyaGusev/ - Original model: https://huggingface.co/IlyaGusev/saiga_gemma_9b/ Original model description: --- language: - ru datasets: - IlyaGusev/ru_turbo_saiga - IlyaGusev/ru_sharegpt_cleaned - IlyaGusev/oasst1_ru_main_branch - IlyaGusev/gpt_roleplay_realm - lksy/ru_instruct_gpt4 --- # Saiga/Gemma 9B, Russian Gemma-based chatbot Based on [Gemma 7B](https://huggingface.co/google/gemma-7b). Training Colab: [link](https://colab.research.google.com/drive/1O7F7Q3IQYh-v7EfsdRwMDMfUnOYZ3DEh). ChatML prompt format: ``` <\|im_start\|>system Ты — Сайга, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им.<\|im_end\|> <\|im_start\|>user Привет!<\|im_end\|> <\|im_start\|>assistant Отлично, а ты?<\|im_end\|> <\|im_start\|>user Шикарно. Как пройти в библиотеку?<\|im_end\|> <\|im_start\|>assistant ``` Examples: ``` User: Почему трава зеленая? Saiga: Зеленый цвет травы обусловлен наличием в её клетках пигмента хлорофилла, который играет важную роль в процессе фотосинтеза – преобразования солнечного света в энергию, используемую для роста и развития растений. Хлорофилл поглощает свет в определенных диапазонах длин волн, в том числе в зеленой области спектра, и отражает свет в других диапазонах, что и обуславливает зелёный цвет. ``` ``` User: Сочини длинный рассказ, обязательно упоминая следующие объекты. Дано: Таня, мяч Saiga: Однажды, в далекой стране, жила маленькая девочка по имени Таня. Она была очень активной и любила играть в мяч. Вместе со своими друзьями они проводили дни, играя в футбол, баскетбол и волейбол. Таня была очень талантливой и ловкой, и она часто выигрывала в играх с другими детьми. Но однажды, во время игры в баскетбол, она получила сильную травму. Ей пришлось провести несколько недель в больнице, и она была очень грустной и унылой. Её друзья не могли понять, как помочь Тане справиться с этой ситуацией. Они пытались поддерживать её, но Таня была слишком подавлена и не хотела играть в мяч. Однажды, Таня увидела, как её друзья играют в мяч. Она не могла не смотреть, как они ловко передают мяч друг другу и забивают голы. Таня почувствовала, как её сердце разрывается от желания играть вместе с ними. Она решила, что ей нужно вернуться в игру, чтобы снова почувствовать радость игры. Она начала медленно восстанавливаться и постепенно возвращалась к своему любимому занятию - игре в мяч. Таня стала играть в мяч снова, но теперь она была более осторожна и внимательна к своему здоровью. Её друзья были очень рады видеть, как Таня возвращается к своей привычной жизни. С тех пор, Таня стала настоящей звездой на поле. Она стала лучшей игроком в своей команде и помогла им выиграть множество игр. Её друзья были горды, что Таня вернулась к своему любимому занятию и снова стала такой же активной и ловкой, как раньше. Таня и её друзья продолжали играть в мяч и проводили чудесные дни вместе. Они были счастливы, что Таня вернулась к своему любимому занятию и снова стала частью команды. Таня и её друзья продолжают играть в мяч и вместе они создают прекрасные воспоминания. ``` v1: - dataset code revision d0d123dd221e10bb2a3383bcb1c6e4efe1b4a28a - wandb [link](https://wandb.ai/ilyagusev/gemma_test/runs/k7u3uw5i) - 5 datasets: ru_turbo_saiga, ru_sharegpt_cleaned, oasst1_ru_main_branch, gpt_roleplay_realm, ru_instruct_gpt4 - Datasets merging script: [create_short_chat_set.py](https://github.com/IlyaGusev/rulm/blob/d0d123dd221e10bb2a3383bcb1c6e4efe1b4a28a/self_instruct/src/data_processing/create_short_chat_set.py)	{}	RichardErkhov/IlyaGusev_-_saiga_gemma_9b-4bits	null	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]	null	2024-04-26T19:08:06+00:00	[]	[]	TAGS #transformers #safetensors #gemma #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us	Quantization made by Richard Erkhov. Github Discord Request more models saiga_gemma_9b - bnb 4bits - Model creator: URL - Original model: URL Original model description: --- language: - ru datasets: - IlyaGusev/ru_turbo_saiga - IlyaGusev/ru_sharegpt_cleaned - IlyaGusev/oasst1_ru_main_branch - IlyaGusev/gpt_roleplay_realm - lksy/ru_instruct_gpt4 --- # Saiga/Gemma 9B, Russian Gemma-based chatbot Based on Gemma 7B. Training Colab: link. ChatML prompt format: Examples: v1: - dataset code revision d0d123dd221e10bb2a3383bcb1c6e4efe1b4a28a - wandb link - 5 datasets: ru_turbo_saiga, ru_sharegpt_cleaned, oasst1_ru_main_branch, gpt_roleplay_realm, ru_instruct_gpt4 - Datasets merging script: create_short_chat_set.py	[ "# Saiga/Gemma 9B, Russian Gemma-based chatbot\n\nBased on Gemma 7B.\n\nTraining Colab: link.\n\nChatML prompt format:\n\n\nExamples:\n\n\n\n\nv1:\n- dataset code revision d0d123dd221e10bb2a3383bcb1c6e4efe1b4a28a\n- wandb link\n- 5 datasets: ru_turbo_saiga, ru_sharegpt_cleaned, oasst1_ru_main_branch, gpt_roleplay_realm, ru_instruct_gpt4\n- Datasets merging script: create_short_chat_set.py" ]	[ "TAGS\n#transformers #safetensors #gemma #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "# Saiga/Gemma 9B, Russian Gemma-based chatbot\n\nBased on Gemma 7B.\n\nTraining Colab: link.\n\nChatML prompt format:\n\n\nExamples:\n\n\n\n\nv1:\n- dataset code revision d0d123dd221e10bb2a3383bcb1c6e4efe1b4a28a\n- wandb link\n- 5 datasets: ru_turbo_saiga, ru_sharegpt_cleaned, oasst1_ru_main_branch, gpt_roleplay_realm, ru_instruct_gpt4\n- Datasets merging script: create_short_chat_set.py" ]
image-text-to-text	null	A [llamafile](https://github.com/Mozilla-Ocho/llamafile) generated for [moondream2](https://huggingface.co/vikhyatk/moondream2) Big thanks to [@jartine](https://huggingface.co/jartine) and [@vikhyat](https://huggingface.co/vikhyatk/moondream2) for their respective works on llamafile and moondream ## How to Run (on macos and linux) 1. Download moondream2.llamafile 2. `chmod +x moondream2.llamafile` - make it executable 3. `./moondream2.llamafile` - run the llama.cpp server ## Versions 1. [Q5_M](https://huggingface.co/cjpais/moondream2-llamafile/resolve/main/moondream2-q5_k.llamafile?download=true) 2. [Q8_0](https://huggingface.co/cjpais/moondream2-llamafile/resolve/main/moondream2-q8.llamafile?download=true) From my short testing the Q8 is noticeably better. # ORIGINAL MODEL CARD moondream2 is a small vision language model designed to run efficiently on edge devices. Check out the [GitHub repository](https://github.com/vikhyat/moondream) for details, or try it out on the [Hugging Face Space](https://huggingface.co/spaces/vikhyatk/moondream2)! Benchmarks \| Release \| VQAv2 \| GQA \| TextVQA \| TallyQA (simple) \| TallyQA (full) \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| 2024-03-04 \| 74.2 \| 58.5 \| 36.4 \| - \| - \| \| 2024-03-06 \| 75.4 \| 59.8 \| 43.1 \| 79.5 \| 73.2 \| \| 2024-03-13 \| 76.8 \| 60.6 \| 46.4 \| 79.6 \| 73.3 \| \| 2024-04-02 (latest) \| 77.7 \| 61.7 \| 49.7 \| 80.1 \| 74.2 \| Usage ```bash pip install transformers einops ``` ```python from transformers import AutoModelForCausalLM, AutoTokenizer from PIL import Image model_id = "vikhyatk/moondream2" revision = "2024-04-02" model = AutoModelForCausalLM.from_pretrained( model_id, trust_remote_code=True, revision=revision ) tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision) image = Image.open('<IMAGE_PATH>') enc_image = model.encode_image(image) print(model.answer_question(enc_image, "Describe this image.", tokenizer)) ``` The model is updated regularly, so we recommend pinning the model version to a specific release as shown above.	{"license": "apache-2.0", "tags": ["llamafile"], "quantized_by": "cjpais", "base_model": "vikhyatk/moondream2", "pipeline_tag": "image-text-to-text"}	cjpais/moondream2-llamafile	null	[ "llamafile", "image-text-to-text", "base_model:vikhyatk/moondream2", "license:apache-2.0", "region:us" ]	null	2024-04-26T19:08:17+00:00	[]	[]	TAGS #llamafile #image-text-to-text #base_model-vikhyatk/moondream2 #license-apache-2.0 #region-us	A llamafile generated for moondream2 Big thanks to @jartine and @vikhyat for their respective works on llamafile and moondream How to Run (on macos and linux) ------------------------------- 1. Download moondream2.llamafile 2. 'chmod +x moondream2.llamafile' - make it executable 3. './moondream2.llamafile' - run the URL server Versions -------- 1. Q5\_M 2. Q8\_0 From my short testing the Q8 is noticeably better. ORIGINAL MODEL CARD =================== moondream2 is a small vision language model designed to run efficiently on edge devices. Check out the GitHub repository for details, or try it out on the Hugging Face Space! Benchmarks Usage The model is updated regularly, so we recommend pinning the model version to a specific release as shown above.	[]	[ "TAGS\n#llamafile #image-text-to-text #base_model-vikhyatk/moondream2 #license-apache-2.0 #region-us \n" ]
text-generation	transformers	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Starling-LM-7B-beta - bnb 4bits - Model creator: https://huggingface.co/Nexusflow/ - Original model: https://huggingface.co/Nexusflow/Starling-LM-7B-beta/ Original model description: --- license: apache-2.0 datasets: - berkeley-nest/Nectar language: - en library_name: transformers tags: - reward model - RLHF - RLAIF --- # Starling-LM-7B-beta <!-- Provide a quick summary of what the model is/does. --> - Developed by: The Nexusflow Team ( Banghua Zhu * , Evan Frick * , Tianhao Wu * , Hanlin Zhu, Karthik Ganesan, Wei-Lin Chiang, Jian Zhang, and Jiantao Jiao). - Model type: Language Model finetuned with RLHF / RLAIF - License: Apache-2.0 license under the condition that the model is not used to compete with OpenAI - Finetuned from model: [Openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106) (based on [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)) We introduce Starling-LM-7B-beta, an open large language model (LLM) trained by Reinforcement Learning from AI Feedback (RLAIF). Starling-LM-7B-beta is trained from [Openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106) with our new reward model [Nexusflow/Starling-RM-34B](https://huggingface.co/Nexusflow/Starling-RM-34B) and policy optimization method [Fine-Tuning Language Models from Human Preferences (PPO)](https://arxiv.org/abs/1909.08593). Harnessing the power of the ranking dataset, [berkeley-nest/Nectar](https://huggingface.co/datasets/berkeley-nest/Nectar), the upgraded reward model, [Starling-RM-34B](https://huggingface.co/Nexusflow/Starling-RM-34B), and the new reward training and policy tuning pipeline, Starling-LM-7B-beta scores an improved 8.12 in MT Bench with GPT-4 as a judge. ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> Important: Please use the exact chat template provided below for the model. Otherwise there will be a degradation in the performance. The model output can be verbose in rare cases. Please consider setting temperature = 0 to make this happen less. Our model follows the exact chat template and usage as [Openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106). Please refer to their model card for more details. In addition, our model is hosted on LMSYS [Chatbot Arena](https://chat.lmsys.org) for free test. The conversation template is the same as Openchat-3.5-0106: ``` import transformers tokenizer = transformers.AutoTokenizer.from_pretrained("openchat/openchat-3.5-0106") # Single-turn tokens = tokenizer("GPT4 Correct User: Hello<\|end_of_turn\|>GPT4 Correct Assistant:").input_ids assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] # Multi-turn tokens = tokenizer("GPT4 Correct User: Hello<\|end_of_turn\|>GPT4 Correct Assistant: Hi<\|end_of_turn\|>GPT4 Correct User: How are you today?<\|end_of_turn\|>GPT4 Correct Assistant:").input_ids assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] # Coding Mode tokens = tokenizer("Code User: Implement quicksort using C++<\|end_of_turn\|>Code Assistant:").input_ids assert tokens == [1, 7596, 1247, 28747, 26256, 2936, 7653, 1413, 334, 1680, 32000, 7596, 21631, 28747] ``` ## Code Examples ```python import transformers tokenizer = transformers.AutoTokenizer.from_pretrained("Nexusflow/Starling-LM-7B-beta") model = transformers.AutoModelForCausalLM.from_pretrained("Nexusflow/Starling-LM-7B-beta") def generate_response(prompt): input_ids = tokenizer(prompt, return_tensors="pt").input_ids outputs = model.generate( input_ids, max_length=256, pad_token_id=tokenizer.pad_token_id, eos_token_id=tokenizer.eos_token_id, ) response_ids = outputs[0] response_text = tokenizer.decode(response_ids, skip_special_tokens=True) return response_text # Single-turn conversation prompt = "Hello, how are you?" single_turn_prompt = f"GPT4 Correct User: {prompt}<\|end_of_turn\|>GPT4 Correct Assistant:" response_text = generate_response(single_turn_prompt) print("Response:", response_text) ## Multi-turn conversation prompt = "Hello" follow_up_question = "How are you today?" response = "" multi_turn_prompt = f"GPT4 Correct User: {prompt}<\|end_of_turn\|>GPT4 Correct Assistant: {response}<\|end_of_turn\|>GPT4 Correct User: {follow_up_question}<\|end_of_turn\|>GPT4 Correct Assistant:" response_text = generate_response(multi_turn_prompt) print("Multi-turn conversation response:", response_text) ### Coding conversation prompt = "Implement quicksort using C++" coding_prompt = f"Code User: {prompt}<\|end_of_turn\|>Code Assistant:" response = generate_response(coding_prompt) print("Coding conversation response:", response) ``` ## License The dataset, model and online demo is subject to the [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI, and [Privacy Practices](https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb) of ShareGPT. Please contact us if you find any potential violation. ## Acknowledgment We would like to thank Tianle Li from UC Berkeley for detailed feedback and evaluation of this beta release. We would like to thank the [LMSYS Organization](https://lmsys.org/) for their support of [lmsys-chat-1M](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) dataset, evaluation and online demo. We would like to thank the open source community for their efforts in providing the datasets and base models we used to develope the project, including but not limited to Anthropic, Llama, Mistral, Hugging Face H4, LMSYS, OpenChat, OpenBMB, Flan and ShareGPT. ## Citation ``` @misc{starling2023, title = {Starling-7B: Improving LLM Helpfulness & Harmlessness with RLAIF}, url = {}, author = {Zhu, Banghua and Frick, Evan and Wu, Tianhao and Zhu, Hanlin and Ganesan, Karthik and Chiang, Wei-Lin and Zhang, Jian and Jiao, Jiantao}, month = {November}, year = {2023} } ```	{}	RichardErkhov/Nexusflow_-_Starling-LM-7B-beta-4bits	null	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1909.08593", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]	null	2024-04-26T19:08:24+00:00	[ "1909.08593" ]	[]	TAGS #transformers #safetensors #mistral #text-generation #conversational #arxiv-1909.08593 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us	Quantization made by Richard Erkhov. Github Discord Request more models Starling-LM-7B-beta - bnb 4bits - Model creator: URL - Original model: URL Original model description: --- license: apache-2.0 datasets: - berkeley-nest/Nectar language: - en library_name: transformers tags: - reward model - RLHF - RLAIF --- # Starling-LM-7B-beta - Developed by: The Nexusflow Team ( Banghua Zhu * , Evan Frick * , Tianhao Wu * , Hanlin Zhu, Karthik Ganesan, Wei-Lin Chiang, Jian Zhang, and Jiantao Jiao). - Model type: Language Model finetuned with RLHF / RLAIF - License: Apache-2.0 license under the condition that the model is not used to compete with OpenAI - Finetuned from model: Openchat-3.5-0106 (based on Mistral-7B-v0.1) We introduce Starling-LM-7B-beta, an open large language model (LLM) trained by Reinforcement Learning from AI Feedback (RLAIF). Starling-LM-7B-beta is trained from Openchat-3.5-0106 with our new reward model Nexusflow/Starling-RM-34B and policy optimization method Fine-Tuning Language Models from Human Preferences (PPO). Harnessing the power of the ranking dataset, berkeley-nest/Nectar, the upgraded reward model, Starling-RM-34B, and the new reward training and policy tuning pipeline, Starling-LM-7B-beta scores an improved 8.12 in MT Bench with GPT-4 as a judge. ## Uses Important: Please use the exact chat template provided below for the model. Otherwise there will be a degradation in the performance. The model output can be verbose in rare cases. Please consider setting temperature = 0 to make this happen less. Our model follows the exact chat template and usage as Openchat-3.5-0106. Please refer to their model card for more details. In addition, our model is hosted on LMSYS Chatbot Arena for free test. The conversation template is the same as Openchat-3.5-0106: ## Code Examples ## License The dataset, model and online demo is subject to the Terms of Use of the data generated by OpenAI, and Privacy Practices of ShareGPT. Please contact us if you find any potential violation. ## Acknowledgment We would like to thank Tianle Li from UC Berkeley for detailed feedback and evaluation of this beta release. We would like to thank the LMSYS Organization for their support of lmsys-chat-1M dataset, evaluation and online demo. We would like to thank the open source community for their efforts in providing the datasets and base models we used to develope the project, including but not limited to Anthropic, Llama, Mistral, Hugging Face H4, LMSYS, OpenChat, OpenBMB, Flan and ShareGPT.	[ "# Starling-LM-7B-beta\n\n\n\n- Developed by: The Nexusflow Team ( Banghua Zhu * , Evan Frick * , Tianhao Wu * , Hanlin Zhu, Karthik Ganesan, Wei-Lin Chiang, Jian Zhang, and Jiantao Jiao).\n- Model type: Language Model finetuned with RLHF / RLAIF\n- License: Apache-2.0 license under the condition that the model is not used to compete with OpenAI\n- Finetuned from model: Openchat-3.5-0106 (based on Mistral-7B-v0.1)\n \n\n\nWe introduce Starling-LM-7B-beta, an open large language model (LLM) trained by Reinforcement Learning from AI Feedback (RLAIF). Starling-LM-7B-beta is trained from Openchat-3.5-0106 with our new reward model Nexusflow/Starling-RM-34B and policy optimization method Fine-Tuning Language Models from Human Preferences (PPO).\nHarnessing the power of the ranking dataset, berkeley-nest/Nectar, the upgraded reward model, Starling-RM-34B, and the new reward training and policy tuning pipeline, Starling-LM-7B-beta scores an improved 8.12 in MT Bench with GPT-4 as a judge.", "## Uses\n\n\n\nImportant: Please use the exact chat template provided below for the model. Otherwise there will be a degradation in the performance. The model output can be verbose in rare cases. Please consider setting temperature = 0 to make this happen less.\n\nOur model follows the exact chat template and usage as Openchat-3.5-0106. Please refer to their model card for more details.\nIn addition, our model is hosted on LMSYS Chatbot Arena for free test.\n\nThe conversation template is the same as Openchat-3.5-0106:", "## Code Examples", "## License\nThe dataset, model and online demo is subject to the Terms of Use of the data generated by OpenAI, and Privacy Practices of ShareGPT. Please contact us if you find any potential violation.", "## Acknowledgment\nWe would like to thank Tianle Li from UC Berkeley for detailed feedback and evaluation of this beta release. We would like to thank the LMSYS Organization for their support of lmsys-chat-1M dataset, evaluation and online demo. We would like to thank the open source community for their efforts in providing the datasets and base models we used to develope the project, including but not limited to Anthropic, Llama, Mistral, Hugging Face H4, LMSYS, OpenChat, OpenBMB, Flan and ShareGPT." ]	[ "TAGS\n#transformers #safetensors #mistral #text-generation #conversational #arxiv-1909.08593 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "# Starling-LM-7B-beta\n\n\n\n- Developed by: The Nexusflow Team ( Banghua Zhu * , Evan Frick * , Tianhao Wu * , Hanlin Zhu, Karthik Ganesan, Wei-Lin Chiang, Jian Zhang, and Jiantao Jiao).\n- Model type: Language Model finetuned with RLHF / RLAIF\n- License: Apache-2.0 license under the condition that the model is not used to compete with OpenAI\n- Finetuned from model: Openchat-3.5-0106 (based on Mistral-7B-v0.1)\n \n\n\nWe introduce Starling-LM-7B-beta, an open large language model (LLM) trained by Reinforcement Learning from AI Feedback (RLAIF). Starling-LM-7B-beta is trained from Openchat-3.5-0106 with our new reward model Nexusflow/Starling-RM-34B and policy optimization method Fine-Tuning Language Models from Human Preferences (PPO).\nHarnessing the power of the ranking dataset, berkeley-nest/Nectar, the upgraded reward model, Starling-RM-34B, and the new reward training and policy tuning pipeline, Starling-LM-7B-beta scores an improved 8.12 in MT Bench with GPT-4 as a judge.", "## Uses\n\n\n\nImportant: Please use the exact chat template provided below for the model. Otherwise there will be a degradation in the performance. The model output can be verbose in rare cases. Please consider setting temperature = 0 to make this happen less.\n\nOur model follows the exact chat template and usage as Openchat-3.5-0106. Please refer to their model card for more details.\nIn addition, our model is hosted on LMSYS Chatbot Arena for free test.\n\nThe conversation template is the same as Openchat-3.5-0106:", "## Code Examples", "## License\nThe dataset, model and online demo is subject to the Terms of Use of the data generated by OpenAI, and Privacy Practices of ShareGPT. Please contact us if you find any potential violation.", "## Acknowledgment\nWe would like to thank Tianle Li from UC Berkeley for detailed feedback and evaluation of this beta release. We would like to thank the LMSYS Organization for their support of lmsys-chat-1M dataset, evaluation and online demo. We would like to thank the open source community for their efforts in providing the datasets and base models we used to develope the project, including but not limited to Anthropic, Llama, Mistral, Hugging Face H4, LMSYS, OpenChat, OpenBMB, Flan and ShareGPT." ]
text-classification	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # results This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4741 - Accuracy: 0.5251 - F1: 0.5348 - Precision: 0.5692 - Recall: 0.5251 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Tokenizers 0.19.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "base_model": "bert-base-uncased", "model-index": [{"name": "results", "results": []}]}	Shreyagg2202/Bert-Custom-Sentiment-Analysis	null	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-26T19:08:47+00:00	[]	[]	TAGS #transformers #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	# results This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4741 - Accuracy: 0.5251 - F1: 0.5348 - Precision: 0.5692 - Recall: 0.5251 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Tokenizers 0.19.1	[ "# results\n\nThis model is a fine-tuned version of bert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.4741\n- Accuracy: 0.5251\n- F1: 0.5348\n- Precision: 0.5692\n- Recall: 0.5251", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.40.1\n- Pytorch 2.2.1+cu121\n- Tokenizers 0.19.1" ]	[ "TAGS\n#transformers #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# results\n\nThis model is a fine-tuned version of bert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.4741\n- Accuracy: 0.5251\n- F1: 0.5348\n- Precision: 0.5692\n- Recall: 0.5251", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.40.1\n- Pytorch 2.2.1+cu121\n- Tokenizers 0.19.1" ]
text-generation	transformers	prompt ``` <original>Ok. What do the drivers look like?</original> <translate to="th"> ``` response ``` <original>กรุงเทพอยู่ที่ไหน</original> <translate to="en">where is bangkok</translate><eos> ``` this model is fail to follow format and output other html (`</input`) sometime at end of text ``` <original>ตำราเรียน (อังกฤษ: Textbook) คือหนังสือที่ใช้ในการศึกษาหาความรู้จากวิชาต่าง ๆ ผู้คนมักใช้ตำราเรียนในการเรียนรู้ข้อเท็จจริงและวิธีการที่เกี่ยวข้องกับรายวิชานั้น ๆ ในบางครั้งตำราเรียนมักมีคำถามเพื่อทดสอบความรู้และความเข้าใจของผู้อ่าน ตำราเรียนจะถูกผลิตจากความต้องการของสถาบันการศึกษา ตำราเรียนส่วนมากมักมีลักษณะเป็นสิ่งพิมพ์ แต่ในปัจจุบันพบว่าหลาย ๆ ตำราเรียนสามารถเข้าถึงได้โดยการออนไลน์ ในรูปแบบของหนังสืออิเล็กทรอนิกส์</original> <translate to="en">Textbooks are books that contain the content of a subject, typically written from an academic viewpoint and intended for use by students. In some countries textbooks can be called "school-book", while in other places they may simply go under this title.</input ```	{"language": ["th", "en"], "datasets": ["scb_mt_enth_2020"], "pipeline_tag": "text-generation"}	ping98k/gemma-7b-translator-0.3	null	[ "transformers", "safetensors", "gguf", "gemma", "text-generation", "th", "en", "dataset:scb_mt_enth_2020", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-26T19:10:39+00:00	[]	[ "th", "en" ]	TAGS #transformers #safetensors #gguf #gemma #text-generation #th #en #dataset-scb_mt_enth_2020 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	prompt response this model is fail to follow format and output other html ('</input') sometime at end of text	[]	[ "TAGS\n#transformers #safetensors #gguf #gemma #text-generation #th #en #dataset-scb_mt_enth_2020 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
null	null	<div align="center"> <img width="260px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/BrQCb95lmEIFz79QAmoNA.png"></div> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/2FhDh8NDvMl7iSxbQz9BP.png) <div align="center"> <h1>Advancing Open-source Large Language Models in Medical Domain</h1> </div> <p align="center" style="margin-top: 0px;"> <a href="https://colab.research.google.com/drive/1F5oV20InEYeAJGmBwYF9NM_QhLmjBkKJ?usp=sharing"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="OpenChat Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 10px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">Online Demo</span> </a> \| <a href="https://github.com/openlifescience-ai"> <img src="https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png" alt="GitHub Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">GitHub</span> </a> \| <a href="#"> <img src="https://github.com/alpayariyak/openchat/blob/master/assets/arxiv-logomark-small-square-border.png?raw=true" alt="ArXiv Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style="margin-right: 5px;">Paper</span> </a> \| <a href="https://discord.gg/A5Fjf5zC69"> <img src="https://cloud.githubusercontent.com/assets/6291467/26705903/96c2d66e-477c-11e7-9f4e-f3c0efe96c9a.png" alt="Discord Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text">Discord</span> </a> </p> ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/KGmRE5w2sepNtwsEu8t7K.jpeg) Introducing OpenBioLLM-8B: A State-of-the-Art Open Source Biomedical Large Language Model OpenBioLLM-8B is an advanced open source language model designed specifically for the biomedical domain. Developed by Saama AI Labs, this model leverages cutting-edge techniques to achieve state-of-the-art performance on a wide range of biomedical tasks. 🏥 Biomedical Specialization: OpenBioLLM-8B is tailored for the unique language and knowledge requirements of the medical and life sciences fields. It was fine-tuned on a vast corpus of high-quality biomedical data, enabling it to understand and generate text with domain-specific accuracy and fluency. 🎓 Superior Performance: With 8 billion parameters, OpenBioLLM-8B outperforms other open source biomedical language models of similar scale. It has also demonstrated better results compared to larger proprietary & open-source models like GPT-3.5 and Meditron-70B on biomedical benchmarks. 🧠 Advanced Training Techniques: OpenBioLLM-8B builds upon the powerful foundations of the Meta-Llama-3-8B and [Meta-Llama-3-8B](meta-llama/Meta-Llama-3-8B) models. It incorporates the DPO dataset and fine-tuning recipe along with a custom diverse medical instruction dataset. Key components of the training pipeline include: <div align="center"> <img width="1200px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/oPchsJsEpQoGcGXVbh7YS.png"> </div> - Policy Optimization: [Direct Preference Optimization: Your Language Model is Secretly a Reward Model (DPO)](https://arxiv.org/abs/2305.18290) - Ranking Dataset: [berkeley-nest/Nectar](https://huggingface.co/datasets/berkeley-nest/Nectar) - Fine-tuning dataset: Custom Medical Instruct dataset (We plan to release a sample training dataset in our upcoming paper; please stay updated) This combination of cutting-edge techniques enables OpenBioLLM-8B to align with key capabilities and preferences for biomedical applications. ⚙️ Release Details: - Model Size: 8 billion parameters - Quantization: Optimized quantized versions available [Here](https://huggingface.co/aaditya/OpenBioLLM-8B-GGUF) - Language(s) (NLP): en - Developed By: [Ankit Pal (Aaditya Ura)](https://aadityaura.github.io/) from Saama AI Labs - License: Meta-Llama License - Fine-tuned from models: [meta-llama/Meta-Llama-3-8B](meta-llama/Meta-Llama-3-8B) - Resources for more information: - Paper: Coming soon The model can be fine-tuned for more specialized tasks and datasets as needed. OpenBioLLM-8B represents an important step forward in democratizing advanced language AI for the biomedical community. By leveraging state-of-the-art architectures and training techniques from leading open source efforts like Llama-3, we have created a powerful tool to accelerate innovation and discovery in healthcare and the life sciences. We are excited to share OpenBioLLM-8B with researchers and developers around the world. ### Use with transformers Important: Please use the exact chat template provided by Llama-3 instruct version. Otherwise there will be a degradation in the performance. The model output can be verbose in rare cases. Please consider setting temperature = 0 to make this happen less. See the snippet below for usage with Transformers: ```python import transformers import torch model_id = "aaditya/OpenBioLLM-Llama3-8B" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device="auto", ) messages = [ {"role": "system", "content": "You are an expert and experienced from the healthcare and biomedical domain with extensive medical knowledge and practical experience. Your name is OpenBioLLM, and you were developed by Saama AI Labs. who's willing to help answer the user's query with explanation. In your explanation, leverage your deep medical expertise such as relevant anatomical structures, physiological processes, diagnostic criteria, treatment guidelines, or other pertinent medical concepts. Use precise medical terminology while still aiming to make the explanation clear and accessible to a general audience."}, {"role": "user", "content": "How can i split a 3mg or 4mg waefin pill so i can get a 2.5mg pill?"}, ] prompt = pipeline.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) terminators = [ pipeline.tokenizer.eos_token_id, pipeline.tokenizer.convert_tokens_to_ids("<\|eot_id\|>") ] outputs = pipeline( prompt, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.0, top_p=0.9, ) print(outputs[0]["generated_text"][len(prompt):]) ``` ## Training procedure ### Training hyperparameters <details> <summary>Click to see details</summary> - learning_rate: 0.0002 - lr_scheduler: cosine - train_batch_size: 12 - eval_batch_size: 8 - GPU: H100 80GB SXM5 - num_devices: 1 - optimizer: adamw_bnb_8bit - lr_scheduler_warmup_steps: 100 - num_epochs: 4 </details> ### Peft hyperparameters <details> <summary>Click to see details</summary> - adapter: qlora - lora_r: 128 - lora_alpha: 256 - lora_dropout: 0.05 - lora_target_linear: true -lora_target_modules: - q_proj - v_proj - k_proj - o_proj - gate_proj - down_proj - up_proj </details> ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.1 - Axolotl - Lm harness for evaluation # Benchmark Results 🔥 OpenBioLLM-8B demonstrates superior performance compared to larger models, such as GPT-3.5, Meditron-70B across 9 diverse biomedical datasets, achieving state-of-the-art results with an average score of 72.50%, despite having a significantly smaller parameter count. The model's strong performance in domain-specific tasks, such as Clinical KG, Medical Genetics, and PubMedQA, highlights its ability to effectively capture and apply biomedical knowledge. 🚨 The GPT-4, Med-PaLM-1, and Med-PaLM-2 results are taken from their official papers. Since Med-PaLM doesn't provide zero-shot accuracy, we are using 5-shot accuracy from their paper for comparison. All results presented are in the zero-shot setting, except for Med-PaLM-2 and Med-PaLM-1, which use 5-shot accuracy. \| \| Clinical KG \| Medical Genetics \| Anatomy \| Pro Medicine \| College Biology \| College Medicine \| MedQA 4 opts \| PubMedQA \| MedMCQA \| Avg \| \|--------------------\|-------------\|------------------\|---------\|--------------\|-----------------\|------------------\|--------------\|----------\|---------\|-------\| \| OpenBioLLM-70B \| 92.93 \| 93.197 \| 83.904 \| 93.75 \| 93.827 \| 85.749 \| 78.162 \| 78.97 \| 74.014 \| 86.05588 \| \| Med-PaLM-2 (5-shot) \| 88.3 \| 90 \| 77.8 \| 95.2 \| 94.4 \| 80.9 \| 79.7 \| 79.2 \| 71.3 \| 84.08 \| \| GPT-4 \| 86.04 \| 91 \| 80 \| 93.01 \| 95.14 \| 76.88 \| 78.87 \| 75.2 \| 69.52 \| 82.85 \| \| Med-PaLM-1 (Flan-PaLM, 5-shot) \| 80.4 \| 75 \| 63.7 \| 83.8 \| 88.9 \| 76.3 \| 67.6 \| 79 \| 57.6 \| 74.7 \| \| OpenBioLLM-8B \| 76.101 \| 86.1 \| 69.829 \| 78.21 \| 84.213 \| 68.042 \| 58.993 \| 74.12 \| 56.913 \| 72.502 \| \| Gemini-1.0 \| 76.7 \| 75.8 \| 66.7 \| 77.7 \| 88 \| 69.2 \| 58 \| 70.7 \| 54.3 \| 70.79 \| \| GPT-3.5 Turbo 1106 \| 74.71 \| 74 \| 72.79 \| 72.79 \| 72.91 \| 64.73 \| 57.71 \| 72.66 \| 53.79 \| 66 \| \| Meditron-70B \| 66.79 \| 69 \| 53.33 \| 71.69 \| 76.38 \| 63 \| 57.1 \| 76.6 \| 46.85 \| 64.52 \| \| gemma-7b \| 69.81 \| 70 \| 59.26 \| 66.18 \| 79.86 \| 60.12 \| 47.21 \| 76.2 \| 48.96 \| 64.18 \| \| Mistral-7B-v0.1 \| 68.68 \| 71 \| 55.56 \| 68.38 \| 68.06 \| 59.54 \| 50.82 \| 75.4 \| 48.2 \| 62.85 \| \| Apollo-7B \| 62.26 \| 72 \| 61.48 \| 69.12 \| 70.83 \| 55.49 \| 55.22 \| 39.8 \| 53.77 \| 60 \| \| MedAlpaca-7b \| 57.36 \| 69 \| 57.04 \| 67.28 \| 65.28 \| 54.34 \| 41.71 \| 72.8 \| 37.51 \| 58.03 \| \| BioMistral-7B \| 59.9 \| 64 \| 56.5 \| 60.4 \| 59 \| 54.7 \| 50.6 \| 77.5 \| 48.1 \| 57.3 \| \| AlpaCare-llama2-7b \| 49.81 \| 49 \| 45.92 \| 33.82 \| 50 \| 43.35 \| 29.77 \| 72.2 \| 34.42 \| 45.36 \| \| ClinicalGPT \| 30.56 \| 27 \| 30.37 \| 19.48 \| 25 \| 24.27 \| 26.08 \| 63.8 \| 28.18 \| 30.52 \| <div align="center"> <img width="1600px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/_SzdcJSBjZyo8RS1bTEkP.png"> </div> ## Detailed Medical Subjectwise accuracy ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/UXF-V0col0Z0sS6BGPBkE.png) # Use Cases & Examples 🚨 Below results are from the quantized version of OpenBioLLM-70B # Summarize Clinical Notes OpenBioLLM-70B can efficiently analyze and summarize complex clinical notes, EHR data, and discharge summaries, extracting key information and generating concise, structured summaries ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/xdwdBgOxNi_TfML0hKlI8.png) # Answer Medical Questions OpenBioLLM-70B can provide answers to a wide range of medical questions. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/zO95GlwOQEZqCKQF69mE6.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/OKBczKw7gWeW5xsuDpc27.png) <details> <summary>Click to see details</summary> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/eJGHT5khppYvJb8fQ-YW4.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/Cnbwrqa_-ORHRuNRC2P6Y.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/J9DhdcvukAc9mnnW9fj2C.png) </details> # Clinical Entity Recognition OpenBioLLM-70B can perform advanced clinical entity recognition by identifying and extracting key medical concepts, such as diseases, symptoms, medications, procedures, and anatomical structures, from unstructured clinical text. By leveraging its deep understanding of medical terminology and context, the model can accurately annotate and categorize clinical entities, enabling more efficient information retrieval, data analysis, and knowledge discovery from electronic health records, research articles, and other biomedical text sources. This capability can support various downstream applications, such as clinical decision support, pharmacovigilance, and medical research. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/_69BW4k9LVABFwtxixL45.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/DKy5wYCoPhoPPUc1-x8_J.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/7WD9zCCBZT4-4XlfnIQjl.png) # Biomarkers Extraction ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/ZttoM4AiteT7gFYVhjIpN.png) # Classification OpenBioLLM-70B can perform various biomedical classification tasks, such as disease prediction, sentiment analysis, medical document categorization ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/Bf5MW1d75qT-1F_TR_hC0.png) # De-Identification OpenBioLLM-70B can detect and remove personally identifiable information (PII) from medical records, ensuring patient privacy and compliance with data protection regulations like HIPAA. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/hKX4kzm--Tw5bj6K78msy.png) Advisory Notice! While OpenBioLLM-70B & 8B leverages high-quality data sources, its outputs may still contain inaccuracies, biases, or misalignments that could pose risks if relied upon for medical decision-making without further testing and refinement. The model's performance has not yet been rigorously evaluated in randomized controlled trials or real-world healthcare environments. Therefore, we strongly advise against using OpenBioLLM-70B & 8B for any direct patient care, clinical decision support, or other professional medical purposes at this time. Its use should be limited to research, development, and exploratory applications by qualified individuals who understand its limitations. OpenBioLLM-70B & 8B are intended solely as a research tool to assist healthcare professionals and should never be considered a replacement for the professional judgment and expertise of a qualified medical doctor. Appropriately adapting and validating OpenBioLLM-70B & 8B for specific medical use cases would require significant additional work, potentially including: - Thorough testing and evaluation in relevant clinical scenarios - Alignment with evidence-based guidelines and best practices - Mitigation of potential biases and failure modes - Integration with human oversight and interpretation - Compliance with regulatory and ethical standards Always consult a qualified healthcare provider for personal medical needs. # Citation If you find OpenBioLLM-70B & 8B useful in your work, please cite the model as follows: ``` @misc{OpenBioLLMs, author = {Ankit Pal, Malaikannan Sankarasubbu}, title = {OpenBioLLMs: Advancing Open-Source Large Language Models for Healthcare and Life Sciences}, year = {2024}, publisher = {Hugging Face}, journal = {Hugging Face repository}, howpublished = {\url{https://huggingface.co/aaditya/OpenBioLLM-Llama3-70B}} } ``` The accompanying paper is currently in progress and will be released soon. <div align="center"> <h2> 💌 Contact </h2> </div> We look forward to hearing you and collaborating on this exciting project! Contributors: - [Ankit Pal (Aaditya Ura)](https://aadityaura.github.io/) [aadityaura at gmail dot com] - Saama AI Labs - Note: I am looking for a funded PhD opportunity, especially if it fits my Responsible Generative AI, Multimodal LLMs, Geometric Deep Learning, and Healthcare AI skillset. # References We thank the [Meta Team](meta-llama/Meta-Llama-3-70B-Instruct) for their amazing models! Result sources - [1] GPT-4 [Capabilities of GPT-4 on Medical Challenge Problems] (https://arxiv.org/abs/2303.13375) - [2] Med-PaLM-1 [Large Language Models Encode Clinical Knowledge](https://arxiv.org/abs/2212.13138) - [3] Med-PaLM-2 [Towards Expert-Level Medical Question Answering with Large Language Models](https://arxiv.org/abs/2305.09617) - [4] Gemini-1.0 [Gemini Goes to Med School](https://arxiv.org/abs/2402.07023)	{"language": ["en"], "license": "llama3", "tags": ["llama-3", "llama", "Mixtral", "instruct", "finetune", "chatml", "DPO", "RLHF", "gpt4", "distillation"], "base_model": "meta-llama/Meta-Llama-3-8B", "widget": [{"example_title": "OpenBioLLM-8B", "messages": [{"role": "system", "content": "You are an expert and experienced from the healthcare and biomedical domain with extensive medical knowledge and practical experience."}, {"role": "user", "content": "How long does it take for newborn jaundice to go away?"}], "output": {"text": "Newborn jaundice, also known as neonatal jaundice, is a common condition in newborns where the yellowing of the skin and eyes occurs due to an elevated level of bilirubin in the blood. Bilirubin is a yellow pigment that forms when red blood cells break down. In most cases, newborn jaundice resolves on its own without any specific treatment.\nThe duration of newborn jaundice can vary depending on several factors such as the underlying cause, gestational age at birth, and individual variations in bilirubin metabolism. Here are some general guidelines:\n1. Physiological jaundice: This is the most common type of newborn jaundice and usually appears within 24-72 hours after birth. It tends to peak between the second and fifth day of life and gradually improves over the next week or two. By the time the baby is one week old, the jaundice should have mostly resolved. 2. Breast milk jaundice: This type of jaundice occurs in breastfed babies and may appear later than physiological jaundice, typically between the fifth and fourteenth day of life. It tends to persist for a longer duration but usually resolves within six weeks after birth. 3. Pathological jaundice: This type of jaundice is less common and occurs due to an underlying medical condition that affects bilirubin metabolism or liver function. The duration of pathological jaundice depends on the specific cause and may require treatment.\nIt's important for parents to monitor their newborn's jaundice closely and seek medical advice if the jaundice progresses rapidly, becomes severe, or is accompanied by other symptoms such as poor feeding, lethargy, or excessive sleepiness. In these cases, further evaluation and management may be necessary. Remember that each baby is unique, and the timing of jaundice resolution can vary. If you have concerns about your newborn's jaundice, it's always best to consult with a healthcare professional for personalized advice and guidance."}}], "model-index": [{"name": "OpenBioLLM-8B", "results": []}]}	LoneStriker/OpenBioLLM-Llama3-8B-GGUF	null	[ "gguf", "llama-3", "llama", "Mixtral", "instruct", "finetune", "chatml", "DPO", "RLHF", "gpt4", "distillation", "en", "arxiv:2305.18290", "arxiv:2303.13375", "arxiv:2212.13138", "arxiv:2305.09617", "arxiv:2402.07023", "base_model:meta-llama/Meta-Llama-3-8B", "license:llama3", "region:us" ]	null	2024-04-26T19:11:19+00:00	[ "2305.18290", "2303.13375", "2212.13138", "2305.09617", "2402.07023" ]	[ "en" ]	TAGS #gguf #llama-3 #llama #Mixtral #instruct #finetune #chatml #DPO #RLHF #gpt4 #distillation #en #arxiv-2305.18290 #arxiv-2303.13375 #arxiv-2212.13138 #arxiv-2305.09617 #arxiv-2402.07023 #base_model-meta-llama/Meta-Llama-3-8B #license-llama3 #region-us	![](URL <p>!image/png</p> <div align=) Advancing Open-source Large Language Models in Medical Domain ============================================================= Online Demo \| GitHub \| [![](URL alt=)](#) \| Discord !image/jpeg Introducing OpenBioLLM-8B: A State-of-the-Art Open Source Biomedical Large Language Model OpenBioLLM-8B is an advanced open source language model designed specifically for the biomedical domain. Developed by Saama AI Labs, this model leverages cutting-edge techniques to achieve state-of-the-art performance on a wide range of biomedical tasks. Biomedical Specialization: OpenBioLLM-8B is tailored for the unique language and knowledge requirements of the medical and life sciences fields. It was fine-tuned on a vast corpus of high-quality biomedical data, enabling it to understand and generate text with domain-specific accuracy and fluency. Superior Performance: With 8 billion parameters, OpenBioLLM-8B outperforms other open source biomedical language models of similar scale. It has also demonstrated better results compared to larger proprietary & open-source models like GPT-3.5 and Meditron-70B on biomedical benchmarks. Advanced Training Techniques: OpenBioLLM-8B builds upon the powerful foundations of the Meta-Llama-3-8B and Meta-Llama-3-8B models. It incorporates the DPO dataset and fine-tuning recipe along with a custom diverse medical instruction dataset. Key components of the training pipeline include: ![](URL </div> <ul> <li>Policy Optimization: Direct Preference Optimization: Your Language Model is Secretly a Reward Model (DPO)</li> <li>Ranking Dataset: berkeley-nest/Nectar</li> <li>Fine-tuning dataset: Custom Medical Instruct dataset (We plan to release a sample training dataset in our upcoming paper; please stay updated)</li> </ul> <p>This combination of cutting-edge techniques enables OpenBioLLM-8B to align with key capabilities and preferences for biomedical applications.</p> <p>️ Release Details:</p> <ul> <li>Model Size: 8 billion parameters</li> <li>Quantization: Optimized quantized versions available Here</li> <li>Language(s) (NLP): en</li> <li>Developed By: Ankit Pal (Aaditya Ura) from Saama AI Labs</li> <li>License: Meta-Llama License</li> <li>Fine-tuned from models: meta-llama/Meta-Llama-3-8B</li> <li>Resources for more information: <ul> <li>Paper: Coming soon</li> </ul> </li> </ul> <p>The model can be fine-tuned for more specialized tasks and datasets as needed.</p> <p>OpenBioLLM-8B represents an important step forward in democratizing advanced language AI for the biomedical community. By leveraging state-of-the-art architectures and training techniques from leading open source efforts like Llama-3, we have created a powerful tool to accelerate innovation and discovery in healthcare and the life sciences.</p> <p>We are excited to share OpenBioLLM-8B with researchers and developers around the world.</p> <h3>Use with transformers</h3> <p>Important: Please use the exact chat template provided by Llama-3 instruct version. Otherwise there will be a degradation in the performance. The model output can be verbose in rare cases. Please consider setting temperature = 0 to make this happen less.</p> <p>See the snippet below for usage with Transformers:</p> <h2>Training procedure</h2> <h3>Training hyperparameters</h3> <details> <summary>Click to see details</summary> <ul> <li>learning_rate: 0.0002</li> <li>lr_scheduler: cosine</li> <li>train_batch_size: 12</li> <li>eval_batch_size: 8</li> <li>GPU: H100 80GB SXM5</li> <li>num_devices: 1</li> <li>optimizer: adamw_bnb_8bit</li> <li>lr_scheduler_warmup_steps: 100</li> <li>num_epochs: 4</li> </ul> </details> <h3>Peft hyperparameters</h3> <details> <summary>Click to see details</summary> <ul> <li>adapter: qlora</li> <li>lora_r: 128</li> <li>lora_alpha: 256</li> <li>lora_dropout: 0.05</li> <li>lora_target_linear: true</li> </ul> <p>-lora_target_modules:</p> <ul> <li>q_proj</li> <li>v_proj</li> <li>k_proj</li> <li>o_proj</li> <li>gate_proj</li> <li>down_proj</li> <li>up_proj</li> </ul> </details> <h3>Training results</h3> <h3>Framework versions</h3> <ul> <li>Transformers 4.39.3</li> <li>Pytorch 2.1.2+cu121</li> <li>Datasets 2.18.0</li> <li>Tokenizers 0.15.1</li> <li>Axolotl</li> <li>Lm harness for evaluation</li> </ul> <h1>Benchmark Results</h1> <p>OpenBioLLM-8B demonstrates superior performance compared to larger models, such as GPT-3.5, Meditron-70B across 9 diverse biomedical datasets, achieving state-of-the-art results with an average score of 72.50%, despite having a significantly smaller parameter count. The model's strong performance in domain-specific tasks, such as Clinical KG, Medical Genetics, and PubMedQA, highlights its ability to effectively capture and apply biomedical knowledge.</p> <p>The GPT-4, Med-PaLM-1, and Med-PaLM-2 results are taken from their official papers. Since Med-PaLM doesn't provide zero-shot accuracy, we are using 5-shot accuracy from their paper for comparison. All results presented are in the zero-shot setting, except for Med-PaLM-2 and Med-PaLM-1, which use 5-shot accuracy.</p> <p></p> <div align=) ![](URL </div> <h2>Detailed Medical Subjectwise accuracy</h2> <p>!image/png</p> <h1>Use Cases & Examples</h1> <p>Below results are from the quantized version of OpenBioLLM-70B</p> <h1>Summarize Clinical Notes</h1> <p>OpenBioLLM-70B can efficiently analyze and summarize complex clinical notes, EHR data, and discharge summaries, extracting key information and generating concise, structured summaries</p> <p>!image/png</p> <h1>Answer Medical Questions</h1> <p>OpenBioLLM-70B can provide answers to a wide range of medical questions.</p> <p>!image/png !image/png</p> <details> <summary>Click to see details</summary> <p>!image/png !image/png !image/png</p> </details> <h1>Clinical Entity Recognition</h1> <p>OpenBioLLM-70B can perform advanced clinical entity recognition by identifying and extracting key medical concepts, such as diseases, symptoms, medications, procedures, and anatomical structures, from unstructured clinical text. By leveraging its deep understanding of medical terminology and context, the model can accurately annotate and categorize clinical entities, enabling more efficient information retrieval, data analysis, and knowledge discovery from electronic health records, research articles, and other biomedical text sources. This capability can support various downstream applications, such as clinical decision support, pharmacovigilance, and medical research.</p> <p>!image/png !image/png !image/png</p> <h1>Biomarkers Extraction</h1> <p>!image/png</p> <h1>Classification</h1> <p>OpenBioLLM-70B can perform various biomedical classification tasks, such as disease prediction, sentiment analysis, medical document categorization</p> <p>!image/png</p> <h1>De-Identification</h1> <p>OpenBioLLM-70B can detect and remove personally identifiable information (PII) from medical records, ensuring patient privacy and compliance with data protection regulations like HIPAA.</p> <p>!image/png</p> <p>Advisory Notice!</p> <p>While OpenBioLLM-70B & 8B leverages high-quality data sources, its outputs may still contain inaccuracies, biases, or misalignments that could pose risks if relied upon for medical decision-making without further testing and refinement. The model's performance has not yet been rigorously evaluated in randomized controlled trials or real-world healthcare environments.</p> <p>Therefore, we strongly advise against using OpenBioLLM-70B & 8B for any direct patient care, clinical decision support, or other professional medical purposes at this time. Its use should be limited to research, development, and exploratory applications by qualified individuals who understand its limitations. OpenBioLLM-70B & 8B are intended solely as a research tool to assist healthcare professionals and should never be considered a replacement for the professional judgment and expertise of a qualified medical doctor.</p> <p>Appropriately adapting and validating OpenBioLLM-70B & 8B for specific medical use cases would require significant additional work, potentially including:</p> <ul> <li>Thorough testing and evaluation in relevant clinical scenarios</li> <li>Alignment with evidence-based guidelines and best practices</li> <li>Mitigation of potential biases and failure modes</li> <li>Integration with human oversight and interpretation</li> <li>Compliance with regulatory and ethical standards</li> </ul> <p>Always consult a qualified healthcare provider for personal medical needs.</p> <p>If you find OpenBioLLM-70B & 8B useful in your work, please cite the model as follows:</p> <p>The accompanying paper is currently in progress and will be released soon.</p> <div align=) Contact -------- We look forward to hearing you and collaborating on this exciting project! Contributors: * Ankit Pal (Aaditya Ura) [aadityaura at gmail dot com] * Saama AI Labs * Note: I am looking for a funded PhD opportunity, especially if it fits my Responsible Generative AI, Multimodal LLMs, Geometric Deep Learning, and Healthcare AI skillset. References ========== We thank the Meta Team for their amazing models! Result sources * [1] GPT-4 [Capabilities of GPT-4 on Medical Challenge Problems] (URL * [2] Med-PaLM-1 Large Language Models Encode Clinical Knowledge * [3] Med-PaLM-2 Towards Expert-Level Medical Question Answering with Large Language Models * [4] Gemini-1.0 Gemini Goes to Med School	[]	[ "TAGS\n#gguf #llama-3 #llama #Mixtral #instruct #finetune #chatml #DPO #RLHF #gpt4 #distillation #en #arxiv-2305.18290 #arxiv-2303.13375 #arxiv-2212.13138 #arxiv-2305.09617 #arxiv-2402.07023 #base_model-meta-llama/Meta-Llama-3-8B #license-llama3 #region-us \n" ]
null	peft	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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text-generation	transformers	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) saiga_llama3_8b - bnb 8bits - Model creator: https://huggingface.co/IlyaGusev/ - Original model: https://huggingface.co/IlyaGusev/saiga_llama3_8b/ Original model description: --- language: - ru datasets: - IlyaGusev/saiga_scored license: other license_name: llama3 license_link: https://llama.meta.com/llama3/license/ --- # Saiga/Llama3 8B, Russian Llama-3-based chatbot Based on [Llama-3 8B Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct). Llama.cpp version: [link](https://huggingface.co/IlyaGusev/saiga_llama3_8b_gguf) ОСТОРОЖНО! WARNING! LET OP! I've changed the prompt format from ChatML to the original Llama-3 format in v4. Don't forget to switch formats! v4: LLama-3 prompt format: ``` <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> Ты — Сайга, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им.<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> Как дела?<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> Отлично, а у тебя?<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> Шикарно. Как пройти в библиотеку?<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> ``` v2, v3: ChatML prompt format: ``` <\|im_start\|>system Ты — Сайга, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им.<\|im_end\|> <\|im_start\|>user Как дела?<\|im_end\|> <\|im_start\|>assistant Отлично, а у тебя?<\|im_end\|> <\|im_start\|>user Шикарно. Как пройти в библиотеку?<\|im_end\|> <\|im_start\|>assistant ``` Examples: ``` User: Почему трава зеленая? Saiga: Зеленый цвет травы обусловлен наличием хлорофилла, пигмента, который поглощает свет и передает энергию для фотосинтеза. Хлорофилл содержится в клетках листьев растений и играет ключевую роль в процессе фотосинтеза, когда растения используют солнечный свет для превращения воды и углекислого газа в органические вещества, такие как глюкоза и олигоэфиры. Хлорофилл состоит из двух основных форм: хлорофилла а и хлорофилла б. Хлорофилл а поглощает свет с длиной волны около 430 нанометров (нм), что соответствует синему или голубому свету, а хлорофилл б поглощает свет с длиной волны около 660 нм, что соответствует красному свету. Когда свет поглощается хлорофиллом, он передается в молекулы воды и углекислого газа, которые используются для фотосинтеза. Таким образом, хлорофилл является основным компонентом, ответственным за зеленый цвет листьев растений, так как он поглощает свет и передает его энергию для фотосинтеза. ``` ``` User: Сочини длинный рассказ, обязательно упоминая следующие объекты. Дано: Таня, мяч Saiga: Однажды в солнечную погоду на поле для игры в футбол приехала группа друзей, чтобы провести время вместе. В числе их был и Таня, которая была страстной футбольной фанаткой и всегда радовал своих друзей своим энтузиазмом и умением забивать мячи. В этот день, как обычно, друзья решили провести соревнование между собой, чтобы определить кто из них является лучшим футболистом. Таня была очень уверена в своих силах и готовилась к этому матчу с особой серьёзностью. Когда все друзья собрались на поле, они увидели, что перед ними стоял огромный мяч, который должен был стать предметом состязания. Мяч был огромным и тяжелым, и его размеры были необычайно большими по сравнению с обычными мячами, которые используются в футболе. Таня была первая, кто решил начать игру. Она подошла к мячу и начала его удерживать, стараясь выдержать его вес и силу. Но мяч оказался настолько тяжелым, что Таня не смогла удержать его и он упал на землю. Друзья посмеялись над ее неудачей, но Таня не отчаивалась и продолжила пытаться удержать мяч. Она стала использовать все свои силы и умения, чтобы выдержать его вес и силу. Наконец, после долгих усилий, она смогла удержать мяч и начала его бросать в сторону. Мяч летел высоко вверх, и друзья смотрели, как он пролетает над полем. Но мяч неожиданно повернул и стал лететь обратно к Тане. Она успела поймать его и продолжила играть, используя все свои навыки и умения. ``` v4: - [1cc945d4ca2c7901cf989e7edaac52ab24f1a7dd](https://huggingface.co/IlyaGusev/saiga_llama3_8b/commit/1cc945d4ca2c7901cf989e7edaac52ab24f1a7dd) - dataset: [saiga_scored](https://huggingface.co/datasets/IlyaGusev/saiga_scored), scores >= 8, c66032920556c0f21bbbed05e7e04433ec954c3d - wandb [link](https://wandb.ai/ilyagusev/rulm_self_instruct/runs/dcbs9ttt) v3: - [c588356cd60bdee54d52c2dd5a2445acca8aa5c3](https://huggingface.co/IlyaGusev/saiga_llama3_8b/commit/c588356cd60bdee54d52c2dd5a2445acca8aa5c3) - dataset: [saiga_scored](https://huggingface.co/datasets/IlyaGusev/saiga_scored), scores >= 8, d51cf8060bdc90023da8cf1c3f113f9193d6569b - wandb [link](https://wandb.ai/ilyagusev/rulm_self_instruct/runs/ltoqdsal) v2: - [ae61b4f9b34fac9856d361ea78c66284a00e4f0b](https://huggingface.co/IlyaGusev/saiga_llama3_8b/commit/ae61b4f9b34fac9856d361ea78c66284a00e4f0b) - dataset code revision d0d123dd221e10bb2a3383bcb1c6e4efe1b4a28a - wandb [link](https://wandb.ai/ilyagusev/huggingface/runs/r6u5juyk) - 5 datasets: ru_turbo_saiga, ru_sharegpt_cleaned, oasst1_ru_main_branch, gpt_roleplay_realm, ru_instruct_gpt4 - Datasets merging script: [create_short_chat_set.py](https://github.com/IlyaGusev/rulm/blob/d0d123dd221e10bb2a3383bcb1c6e4efe1b4a28a/self_instruct/src/data_processing/create_short_chat_set.py) # Evaluation * Dataset: https://github.com/IlyaGusev/rulm/blob/master/self_instruct/data/tasks.jsonl * Framework: https://github.com/tatsu-lab/alpaca_eval * Evaluator: alpaca_eval_cot_gpt4_turbo_fn \| model \| length_controlled_winrate \| win_rate \| standard_error \| avg_length \| \|-----\|-----\|-----\|-----\|-----\| \|chatgpt_4_turbo \| 76.04 \| 90.00 \|1.46 \| 1270 \| \|chatgpt_3_5_turbo \| 50.00 \| 50.00 \| 0.00 \| 536 \| \|saiga_llama3_8b, v4 \| 43.64 \| 65.90 \| 2.31 \| 1200 \| \|saiga_llama3_8b, v3 \| 36.97 \| 61.08 \| 2.38 \| 1162 \| \|saiga_llama3_8b, v2 \| 33.07 \| 48.19 \| 2.45 \| 1166 \| \|saiga_mistral_7b \| 23.38 \| 35.99 \| 2.34 \| 949 \|	{}	RichardErkhov/IlyaGusev_-_saiga_llama3_8b-8bits	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "8-bit", "region:us" ]	null	2024-04-26T19:13:40+00:00	[]	[]	TAGS #transformers #safetensors #llama #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us	Quantization made by Richard Erkhov. Github Discord Request more models saiga\_llama3\_8b - bnb 8bits * Model creator: URL * Original model: URL Original model description: --------------------------- language: * ru datasets: * IlyaGusev/saiga\_scored license: other license\_name: llama3 license\_link: URL --- Saiga/Llama3 8B, Russian Llama-3-based chatbot ============================================== Based on Llama-3 8B Instruct. URL version: link ОСТОРОЖНО! WARNING! LET OP! I've changed the prompt format from ChatML to the original Llama-3 format in v4. Don't forget to switch formats! v4: LLama-3 prompt format: v2, v3: ChatML prompt format: Examples: v4: * 1cc945d4ca2c7901cf989e7edaac52ab24f1a7dd * dataset: saiga\_scored, scores >= 8, c66032920556c0f21bbbed05e7e04433ec954c3d * wandb link v3: * c588356cd60bdee54d52c2dd5a2445acca8aa5c3 * dataset: saiga\_scored, scores >= 8, d51cf8060bdc90023da8cf1c3f113f9193d6569b * wandb link v2: * ae61b4f9b34fac9856d361ea78c66284a00e4f0b * dataset code revision d0d123dd221e10bb2a3383bcb1c6e4efe1b4a28a * wandb link * 5 datasets: ru\_turbo\_saiga, ru\_sharegpt\_cleaned, oasst1\_ru\_main\_branch, gpt\_roleplay\_realm, ru\_instruct\_gpt4 * Datasets merging script: create\_short\_chat\_set.py Evaluation ========== * Dataset: URL * Framework: URL * Evaluator: alpaca\_eval\_cot\_gpt4\_turbo\_fn	[]	[ "TAGS\n#transformers #safetensors #llama #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us \n" ]
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": ["unsloth", "trl", "sft"]}	cashu/mistral_16_bit	null	[ "transformers", "safetensors", "mistral", "text-generation", "unsloth", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-26T19:13:49+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #mistral #text-generation #unsloth #trl #sft #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. 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This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #mistral #text-generation #unsloth #trl #sft #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
text-generation	transformers	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) saiga_gemma_9b - bnb 8bits - Model creator: https://huggingface.co/IlyaGusev/ - Original model: https://huggingface.co/IlyaGusev/saiga_gemma_9b/ Original model description: --- language: - ru datasets: - IlyaGusev/ru_turbo_saiga - IlyaGusev/ru_sharegpt_cleaned - IlyaGusev/oasst1_ru_main_branch - IlyaGusev/gpt_roleplay_realm - lksy/ru_instruct_gpt4 --- # Saiga/Gemma 9B, Russian Gemma-based chatbot Based on [Gemma 7B](https://huggingface.co/google/gemma-7b). Training Colab: [link](https://colab.research.google.com/drive/1O7F7Q3IQYh-v7EfsdRwMDMfUnOYZ3DEh). ChatML prompt format: ``` <\|im_start\|>system Ты — Сайга, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им.<\|im_end\|> <\|im_start\|>user Привет!<\|im_end\|> <\|im_start\|>assistant Отлично, а ты?<\|im_end\|> <\|im_start\|>user Шикарно. Как пройти в библиотеку?<\|im_end\|> <\|im_start\|>assistant ``` Examples: ``` User: Почему трава зеленая? Saiga: Зеленый цвет травы обусловлен наличием в её клетках пигмента хлорофилла, который играет важную роль в процессе фотосинтеза – преобразования солнечного света в энергию, используемую для роста и развития растений. Хлорофилл поглощает свет в определенных диапазонах длин волн, в том числе в зеленой области спектра, и отражает свет в других диапазонах, что и обуславливает зелёный цвет. ``` ``` User: Сочини длинный рассказ, обязательно упоминая следующие объекты. Дано: Таня, мяч Saiga: Однажды, в далекой стране, жила маленькая девочка по имени Таня. Она была очень активной и любила играть в мяч. Вместе со своими друзьями они проводили дни, играя в футбол, баскетбол и волейбол. Таня была очень талантливой и ловкой, и она часто выигрывала в играх с другими детьми. Но однажды, во время игры в баскетбол, она получила сильную травму. Ей пришлось провести несколько недель в больнице, и она была очень грустной и унылой. Её друзья не могли понять, как помочь Тане справиться с этой ситуацией. Они пытались поддерживать её, но Таня была слишком подавлена и не хотела играть в мяч. Однажды, Таня увидела, как её друзья играют в мяч. Она не могла не смотреть, как они ловко передают мяч друг другу и забивают голы. Таня почувствовала, как её сердце разрывается от желания играть вместе с ними. Она решила, что ей нужно вернуться в игру, чтобы снова почувствовать радость игры. Она начала медленно восстанавливаться и постепенно возвращалась к своему любимому занятию - игре в мяч. Таня стала играть в мяч снова, но теперь она была более осторожна и внимательна к своему здоровью. Её друзья были очень рады видеть, как Таня возвращается к своей привычной жизни. С тех пор, Таня стала настоящей звездой на поле. Она стала лучшей игроком в своей команде и помогла им выиграть множество игр. Её друзья были горды, что Таня вернулась к своему любимому занятию и снова стала такой же активной и ловкой, как раньше. Таня и её друзья продолжали играть в мяч и проводили чудесные дни вместе. Они были счастливы, что Таня вернулась к своему любимому занятию и снова стала частью команды. Таня и её друзья продолжают играть в мяч и вместе они создают прекрасные воспоминания. ``` v1: - dataset code revision d0d123dd221e10bb2a3383bcb1c6e4efe1b4a28a - wandb [link](https://wandb.ai/ilyagusev/gemma_test/runs/k7u3uw5i) - 5 datasets: ru_turbo_saiga, ru_sharegpt_cleaned, oasst1_ru_main_branch, gpt_roleplay_realm, ru_instruct_gpt4 - Datasets merging script: [create_short_chat_set.py](https://github.com/IlyaGusev/rulm/blob/d0d123dd221e10bb2a3383bcb1c6e4efe1b4a28a/self_instruct/src/data_processing/create_short_chat_set.py)	{}	RichardErkhov/IlyaGusev_-_saiga_gemma_9b-8bits	null	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "8-bit", "region:us" ]	null	2024-04-26T19:14:13+00:00	[]	[]	TAGS #transformers #safetensors #gemma #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us	Quantization made by Richard Erkhov. Github Discord Request more models saiga_gemma_9b - bnb 8bits - Model creator: URL - Original model: URL Original model description: --- language: - ru datasets: - IlyaGusev/ru_turbo_saiga - IlyaGusev/ru_sharegpt_cleaned - IlyaGusev/oasst1_ru_main_branch - IlyaGusev/gpt_roleplay_realm - lksy/ru_instruct_gpt4 --- # Saiga/Gemma 9B, Russian Gemma-based chatbot Based on Gemma 7B. Training Colab: link. ChatML prompt format: Examples: v1: - dataset code revision d0d123dd221e10bb2a3383bcb1c6e4efe1b4a28a - wandb link - 5 datasets: ru_turbo_saiga, ru_sharegpt_cleaned, oasst1_ru_main_branch, gpt_roleplay_realm, ru_instruct_gpt4 - Datasets merging script: create_short_chat_set.py	[ "# Saiga/Gemma 9B, Russian Gemma-based chatbot\n\nBased on Gemma 7B.\n\nTraining Colab: link.\n\nChatML prompt format:\n\n\nExamples:\n\n\n\n\nv1:\n- dataset code revision d0d123dd221e10bb2a3383bcb1c6e4efe1b4a28a\n- wandb link\n- 5 datasets: ru_turbo_saiga, ru_sharegpt_cleaned, oasst1_ru_main_branch, gpt_roleplay_realm, ru_instruct_gpt4\n- Datasets merging script: create_short_chat_set.py" ]	[ "TAGS\n#transformers #safetensors #gemma #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us \n", "# Saiga/Gemma 9B, Russian Gemma-based chatbot\n\nBased on Gemma 7B.\n\nTraining Colab: link.\n\nChatML prompt format:\n\n\nExamples:\n\n\n\n\nv1:\n- dataset code revision d0d123dd221e10bb2a3383bcb1c6e4efe1b4a28a\n- wandb link\n- 5 datasets: ru_turbo_saiga, ru_sharegpt_cleaned, oasst1_ru_main_branch, gpt_roleplay_realm, ru_instruct_gpt4\n- Datasets merging script: create_short_chat_set.py" ]
null	null	White teeth smiles alot to hide her pain African American Eyes blue as the deep sea a lot of curly hair Can speak more than 5 languages Is just looking for peace and quiet Desires to be a sloth but have a lot of money	{}	Ytsheeqee/meta-verse-creation	null	[ "region:us" ]	null	2024-04-26T19:14:51+00:00	[]	[]	TAGS #region-us	White teeth smiles alot to hide her pain African American Eyes blue as the deep sea a lot of curly hair Can speak more than 5 languages Is just looking for peace and quiet Desires to be a sloth but have a lot of money	[]	[ "TAGS\n#region-us \n" ]
null	transformers	## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/chujiezheng/tulu-2-dpo-70b-ExPO <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/tulu-2-dpo-70b-ExPO-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-70b-ExPO-GGUF/resolve/main/tulu-2-dpo-70b-ExPO.Q2_K.gguf) \| Q2_K \| 25.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-70b-ExPO-GGUF/resolve/main/tulu-2-dpo-70b-ExPO.IQ3_XS.gguf) \| IQ3_XS \| 28.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-70b-ExPO-GGUF/resolve/main/tulu-2-dpo-70b-ExPO.IQ3_S.gguf) \| IQ3_S \| 30.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-70b-ExPO-GGUF/resolve/main/tulu-2-dpo-70b-ExPO.Q3_K_S.gguf) \| Q3_K_S \| 30.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-70b-ExPO-GGUF/resolve/main/tulu-2-dpo-70b-ExPO.IQ3_M.gguf) \| IQ3_M \| 31.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-70b-ExPO-GGUF/resolve/main/tulu-2-dpo-70b-ExPO.Q3_K_M.gguf) \| Q3_K_M \| 33.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-70b-ExPO-GGUF/resolve/main/tulu-2-dpo-70b-ExPO.Q3_K_L.gguf) \| Q3_K_L \| 36.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-70b-ExPO-GGUF/resolve/main/tulu-2-dpo-70b-ExPO.IQ4_XS.gguf) \| IQ4_XS \| 37.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-70b-ExPO-GGUF/resolve/main/tulu-2-dpo-70b-ExPO.Q4_K_S.gguf) \| Q4_K_S \| 39.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-70b-ExPO-GGUF/resolve/main/tulu-2-dpo-70b-ExPO.Q4_K_M.gguf) \| Q4_K_M \| 41.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-70b-ExPO-GGUF/resolve/main/tulu-2-dpo-70b-ExPO.Q5_K_S.gguf) \| Q5_K_S \| 47.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-70b-ExPO-GGUF/resolve/main/tulu-2-dpo-70b-ExPO.Q5_K_M.gguf) \| Q5_K_M \| 48.9 \| \| \| [PART 1](https://huggingface.co/mradermacher/tulu-2-dpo-70b-ExPO-GGUF/resolve/main/tulu-2-dpo-70b-ExPO.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/tulu-2-dpo-70b-ExPO-GGUF/resolve/main/tulu-2-dpo-70b-ExPO.Q6_K.gguf.part2of2) \| Q6_K \| 56.7 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/tulu-2-dpo-70b-ExPO-GGUF/resolve/main/tulu-2-dpo-70b-ExPO.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/tulu-2-dpo-70b-ExPO-GGUF/resolve/main/tulu-2-dpo-70b-ExPO.Q8_0.gguf.part2of2) \| Q8_0 \| 73.4 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->	{"language": ["en"], "license": "other", "library_name": "transformers", "base_model": "chujiezheng/tulu-2-dpo-70b-ExPO", "license_link": "https://allenai.org/impact-license", "license_name": "ai2-impact-license-low-risk", "quantized_by": "mradermacher"}	mradermacher/tulu-2-dpo-70b-ExPO-GGUF	null	[ "transformers", "gguf", "en", "base_model:chujiezheng/tulu-2-dpo-70b-ExPO", "license:other", "endpoints_compatible", "region:us" ]	null	2024-04-26T19:16:11+00:00	[]	[ "en" ]	TAGS #transformers #gguf #en #base_model-chujiezheng/tulu-2-dpo-70b-ExPO #license-other #endpoints_compatible #region-us	About ----- static quants of URL weighted/imatrix quants are available at URL Usage ----- If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. Provided Quants --------------- (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): !URL And here are Artefact2's thoughts on the matter: URL FAQ / Model Request ------------------- See URL for some answers to questions you might have and/or if you want some other model quantized. Thanks ------ I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.	[]	[ "TAGS\n#transformers #gguf #en #base_model-chujiezheng/tulu-2-dpo-70b-ExPO #license-other #endpoints_compatible #region-us \n" ]
text-generation	transformers	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Starling-LM-7B-beta - bnb 8bits - Model creator: https://huggingface.co/Nexusflow/ - Original model: https://huggingface.co/Nexusflow/Starling-LM-7B-beta/ Original model description: --- license: apache-2.0 datasets: - berkeley-nest/Nectar language: - en library_name: transformers tags: - reward model - RLHF - RLAIF --- # Starling-LM-7B-beta <!-- Provide a quick summary of what the model is/does. --> - Developed by: The Nexusflow Team ( Banghua Zhu * , Evan Frick * , Tianhao Wu * , Hanlin Zhu, Karthik Ganesan, Wei-Lin Chiang, Jian Zhang, and Jiantao Jiao). - Model type: Language Model finetuned with RLHF / RLAIF - License: Apache-2.0 license under the condition that the model is not used to compete with OpenAI - Finetuned from model: [Openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106) (based on [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)) We introduce Starling-LM-7B-beta, an open large language model (LLM) trained by Reinforcement Learning from AI Feedback (RLAIF). Starling-LM-7B-beta is trained from [Openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106) with our new reward model [Nexusflow/Starling-RM-34B](https://huggingface.co/Nexusflow/Starling-RM-34B) and policy optimization method [Fine-Tuning Language Models from Human Preferences (PPO)](https://arxiv.org/abs/1909.08593). Harnessing the power of the ranking dataset, [berkeley-nest/Nectar](https://huggingface.co/datasets/berkeley-nest/Nectar), the upgraded reward model, [Starling-RM-34B](https://huggingface.co/Nexusflow/Starling-RM-34B), and the new reward training and policy tuning pipeline, Starling-LM-7B-beta scores an improved 8.12 in MT Bench with GPT-4 as a judge. ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> Important: Please use the exact chat template provided below for the model. Otherwise there will be a degradation in the performance. The model output can be verbose in rare cases. Please consider setting temperature = 0 to make this happen less. Our model follows the exact chat template and usage as [Openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106). Please refer to their model card for more details. In addition, our model is hosted on LMSYS [Chatbot Arena](https://chat.lmsys.org) for free test. The conversation template is the same as Openchat-3.5-0106: ``` import transformers tokenizer = transformers.AutoTokenizer.from_pretrained("openchat/openchat-3.5-0106") # Single-turn tokens = tokenizer("GPT4 Correct User: Hello<\|end_of_turn\|>GPT4 Correct Assistant:").input_ids assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] # Multi-turn tokens = tokenizer("GPT4 Correct User: Hello<\|end_of_turn\|>GPT4 Correct Assistant: Hi<\|end_of_turn\|>GPT4 Correct User: How are you today?<\|end_of_turn\|>GPT4 Correct Assistant:").input_ids assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] # Coding Mode tokens = tokenizer("Code User: Implement quicksort using C++<\|end_of_turn\|>Code Assistant:").input_ids assert tokens == [1, 7596, 1247, 28747, 26256, 2936, 7653, 1413, 334, 1680, 32000, 7596, 21631, 28747] ``` ## Code Examples ```python import transformers tokenizer = transformers.AutoTokenizer.from_pretrained("Nexusflow/Starling-LM-7B-beta") model = transformers.AutoModelForCausalLM.from_pretrained("Nexusflow/Starling-LM-7B-beta") def generate_response(prompt): input_ids = tokenizer(prompt, return_tensors="pt").input_ids outputs = model.generate( input_ids, max_length=256, pad_token_id=tokenizer.pad_token_id, eos_token_id=tokenizer.eos_token_id, ) response_ids = outputs[0] response_text = tokenizer.decode(response_ids, skip_special_tokens=True) return response_text # Single-turn conversation prompt = "Hello, how are you?" single_turn_prompt = f"GPT4 Correct User: {prompt}<\|end_of_turn\|>GPT4 Correct Assistant:" response_text = generate_response(single_turn_prompt) print("Response:", response_text) ## Multi-turn conversation prompt = "Hello" follow_up_question = "How are you today?" response = "" multi_turn_prompt = f"GPT4 Correct User: {prompt}<\|end_of_turn\|>GPT4 Correct Assistant: {response}<\|end_of_turn\|>GPT4 Correct User: {follow_up_question}<\|end_of_turn\|>GPT4 Correct Assistant:" response_text = generate_response(multi_turn_prompt) print("Multi-turn conversation response:", response_text) ### Coding conversation prompt = "Implement quicksort using C++" coding_prompt = f"Code User: {prompt}<\|end_of_turn\|>Code Assistant:" response = generate_response(coding_prompt) print("Coding conversation response:", response) ``` ## License The dataset, model and online demo is subject to the [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI, and [Privacy Practices](https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb) of ShareGPT. Please contact us if you find any potential violation. ## Acknowledgment We would like to thank Tianle Li from UC Berkeley for detailed feedback and evaluation of this beta release. We would like to thank the [LMSYS Organization](https://lmsys.org/) for their support of [lmsys-chat-1M](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) dataset, evaluation and online demo. We would like to thank the open source community for their efforts in providing the datasets and base models we used to develope the project, including but not limited to Anthropic, Llama, Mistral, Hugging Face H4, LMSYS, OpenChat, OpenBMB, Flan and ShareGPT. ## Citation ``` @misc{starling2023, title = {Starling-7B: Improving LLM Helpfulness & Harmlessness with RLAIF}, url = {}, author = {Zhu, Banghua and Frick, Evan and Wu, Tianhao and Zhu, Hanlin and Ganesan, Karthik and Chiang, Wei-Lin and Zhang, Jian and Jiao, Jiantao}, month = {November}, year = {2023} } ```	{}	RichardErkhov/Nexusflow_-_Starling-LM-7B-beta-8bits	null	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1909.08593", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "8-bit", "region:us" ]	null	2024-04-26T19:17:31+00:00	[ "1909.08593" ]	[]	TAGS #transformers #safetensors #mistral #text-generation #conversational #arxiv-1909.08593 #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us	Quantization made by Richard Erkhov. Github Discord Request more models Starling-LM-7B-beta - bnb 8bits - Model creator: URL - Original model: URL Original model description: --- license: apache-2.0 datasets: - berkeley-nest/Nectar language: - en library_name: transformers tags: - reward model - RLHF - RLAIF --- # Starling-LM-7B-beta - Developed by: The Nexusflow Team ( Banghua Zhu * , Evan Frick * , Tianhao Wu * , Hanlin Zhu, Karthik Ganesan, Wei-Lin Chiang, Jian Zhang, and Jiantao Jiao). - Model type: Language Model finetuned with RLHF / RLAIF - License: Apache-2.0 license under the condition that the model is not used to compete with OpenAI - Finetuned from model: Openchat-3.5-0106 (based on Mistral-7B-v0.1) We introduce Starling-LM-7B-beta, an open large language model (LLM) trained by Reinforcement Learning from AI Feedback (RLAIF). Starling-LM-7B-beta is trained from Openchat-3.5-0106 with our new reward model Nexusflow/Starling-RM-34B and policy optimization method Fine-Tuning Language Models from Human Preferences (PPO). Harnessing the power of the ranking dataset, berkeley-nest/Nectar, the upgraded reward model, Starling-RM-34B, and the new reward training and policy tuning pipeline, Starling-LM-7B-beta scores an improved 8.12 in MT Bench with GPT-4 as a judge. ## Uses Important: Please use the exact chat template provided below for the model. Otherwise there will be a degradation in the performance. The model output can be verbose in rare cases. Please consider setting temperature = 0 to make this happen less. Our model follows the exact chat template and usage as Openchat-3.5-0106. Please refer to their model card for more details. In addition, our model is hosted on LMSYS Chatbot Arena for free test. The conversation template is the same as Openchat-3.5-0106: ## Code Examples ## License The dataset, model and online demo is subject to the Terms of Use of the data generated by OpenAI, and Privacy Practices of ShareGPT. Please contact us if you find any potential violation. ## Acknowledgment We would like to thank Tianle Li from UC Berkeley for detailed feedback and evaluation of this beta release. We would like to thank the LMSYS Organization for their support of lmsys-chat-1M dataset, evaluation and online demo. We would like to thank the open source community for their efforts in providing the datasets and base models we used to develope the project, including but not limited to Anthropic, Llama, Mistral, Hugging Face H4, LMSYS, OpenChat, OpenBMB, Flan and ShareGPT.	[ "# Starling-LM-7B-beta\n\n\n\n- Developed by: The Nexusflow Team ( Banghua Zhu * , Evan Frick * , Tianhao Wu * , Hanlin Zhu, Karthik Ganesan, Wei-Lin Chiang, Jian Zhang, and Jiantao Jiao).\n- Model type: Language Model finetuned with RLHF / RLAIF\n- License: Apache-2.0 license under the condition that the model is not used to compete with OpenAI\n- Finetuned from model: Openchat-3.5-0106 (based on Mistral-7B-v0.1)\n \n\n\nWe introduce Starling-LM-7B-beta, an open large language model (LLM) trained by Reinforcement Learning from AI Feedback (RLAIF). Starling-LM-7B-beta is trained from Openchat-3.5-0106 with our new reward model Nexusflow/Starling-RM-34B and policy optimization method Fine-Tuning Language Models from Human Preferences (PPO).\nHarnessing the power of the ranking dataset, berkeley-nest/Nectar, the upgraded reward model, Starling-RM-34B, and the new reward training and policy tuning pipeline, Starling-LM-7B-beta scores an improved 8.12 in MT Bench with GPT-4 as a judge.", "## Uses\n\n\n\nImportant: Please use the exact chat template provided below for the model. Otherwise there will be a degradation in the performance. The model output can be verbose in rare cases. Please consider setting temperature = 0 to make this happen less.\n\nOur model follows the exact chat template and usage as Openchat-3.5-0106. Please refer to their model card for more details.\nIn addition, our model is hosted on LMSYS Chatbot Arena for free test.\n\nThe conversation template is the same as Openchat-3.5-0106:", "## Code Examples", "## License\nThe dataset, model and online demo is subject to the Terms of Use of the data generated by OpenAI, and Privacy Practices of ShareGPT. Please contact us if you find any potential violation.", "## Acknowledgment\nWe would like to thank Tianle Li from UC Berkeley for detailed feedback and evaluation of this beta release. We would like to thank the LMSYS Organization for their support of lmsys-chat-1M dataset, evaluation and online demo. We would like to thank the open source community for their efforts in providing the datasets and base models we used to develope the project, including but not limited to Anthropic, Llama, Mistral, Hugging Face H4, LMSYS, OpenChat, OpenBMB, Flan and ShareGPT." ]	[ "TAGS\n#transformers #safetensors #mistral #text-generation #conversational #arxiv-1909.08593 #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us \n", "# Starling-LM-7B-beta\n\n\n\n- Developed by: The Nexusflow Team ( Banghua Zhu * , Evan Frick * , Tianhao Wu * , Hanlin Zhu, Karthik Ganesan, Wei-Lin Chiang, Jian Zhang, and Jiantao Jiao).\n- Model type: Language Model finetuned with RLHF / RLAIF\n- License: Apache-2.0 license under the condition that the model is not used to compete with OpenAI\n- Finetuned from model: Openchat-3.5-0106 (based on Mistral-7B-v0.1)\n \n\n\nWe introduce Starling-LM-7B-beta, an open large language model (LLM) trained by Reinforcement Learning from AI Feedback (RLAIF). Starling-LM-7B-beta is trained from Openchat-3.5-0106 with our new reward model Nexusflow/Starling-RM-34B and policy optimization method Fine-Tuning Language Models from Human Preferences (PPO).\nHarnessing the power of the ranking dataset, berkeley-nest/Nectar, the upgraded reward model, Starling-RM-34B, and the new reward training and policy tuning pipeline, Starling-LM-7B-beta scores an improved 8.12 in MT Bench with GPT-4 as a judge.", "## Uses\n\n\n\nImportant: Please use the exact chat template provided below for the model. Otherwise there will be a degradation in the performance. The model output can be verbose in rare cases. Please consider setting temperature = 0 to make this happen less.\n\nOur model follows the exact chat template and usage as Openchat-3.5-0106. Please refer to their model card for more details.\nIn addition, our model is hosted on LMSYS Chatbot Arena for free test.\n\nThe conversation template is the same as Openchat-3.5-0106:", "## Code Examples", "## License\nThe dataset, model and online demo is subject to the Terms of Use of the data generated by OpenAI, and Privacy Practices of ShareGPT. Please contact us if you find any potential violation.", "## Acknowledgment\nWe would like to thank Tianle Li from UC Berkeley for detailed feedback and evaluation of this beta release. We would like to thank the LMSYS Organization for their support of lmsys-chat-1M dataset, evaluation and online demo. We would like to thank the open source community for their efforts in providing the datasets and base models we used to develope the project, including but not limited to Anthropic, Llama, Mistral, Hugging Face H4, LMSYS, OpenChat, OpenBMB, Flan and ShareGPT." ]
null	transformers	## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/Dunjeon/lostmagic-RP_7B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/lostmagic-RP_7B-GGUF/resolve/main/lostmagic-RP_7B.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/lostmagic-RP_7B-GGUF/resolve/main/lostmagic-RP_7B.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/lostmagic-RP_7B-GGUF/resolve/main/lostmagic-RP_7B.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/lostmagic-RP_7B-GGUF/resolve/main/lostmagic-RP_7B.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/lostmagic-RP_7B-GGUF/resolve/main/lostmagic-RP_7B.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/lostmagic-RP_7B-GGUF/resolve/main/lostmagic-RP_7B.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/lostmagic-RP_7B-GGUF/resolve/main/lostmagic-RP_7B.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/lostmagic-RP_7B-GGUF/resolve/main/lostmagic-RP_7B.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/lostmagic-RP_7B-GGUF/resolve/main/lostmagic-RP_7B.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/lostmagic-RP_7B-GGUF/resolve/main/lostmagic-RP_7B.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/lostmagic-RP_7B-GGUF/resolve/main/lostmagic-RP_7B.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/lostmagic-RP_7B-GGUF/resolve/main/lostmagic-RP_7B.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/lostmagic-RP_7B-GGUF/resolve/main/lostmagic-RP_7B.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/lostmagic-RP_7B-GGUF/resolve/main/lostmagic-RP_7B.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/lostmagic-RP_7B-GGUF/resolve/main/lostmagic-RP_7B.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->	{"language": ["en"], "license": "cc-by-nc-4.0", "library_name": "transformers", "tags": ["roleplay", "uncensored", "lewd", "mature", "not-for-all-audiences"], "base_model": "Dunjeon/lostmagic-RP_7B", "quantized_by": "mradermacher"}	mradermacher/lostmagic-RP_7B-GGUF	null	[ "transformers", "gguf", "roleplay", "uncensored", "lewd", "mature", "not-for-all-audiences", "en", "base_model:Dunjeon/lostmagic-RP_7B", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	null	2024-04-26T19:19:10+00:00	[]	[ "en" ]	TAGS #transformers #gguf #roleplay #uncensored #lewd #mature #not-for-all-audiences #en #base_model-Dunjeon/lostmagic-RP_7B #license-cc-by-nc-4.0 #endpoints_compatible #region-us	About ----- static quants of URL weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. Usage ----- If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. Provided Quants --------------- (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): !URL And here are Artefact2's thoughts on the matter: URL FAQ / Model Request ------------------- See URL for some answers to questions you might have and/or if you want some other model quantized. Thanks ------ I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.	[]	[ "TAGS\n#transformers #gguf #roleplay #uncensored #lewd #mature #not-for-all-audiences #en #base_model-Dunjeon/lostmagic-RP_7B #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n" ]
image-classification	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Boya1_RMSProp_1-e5_10Epoch_swin-large-patch4-window7-224_fold1 This model is a fine-tuned version of [microsoft/swin-large-patch4-window7-224](https://huggingface.co/microsoft/swin-large-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.1733 - Accuracy: 0.6654 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.1578 \| 1.0 \| 924 \| 1.1827 \| 0.5935 \| \| 0.9384 \| 2.0 \| 1848 \| 1.0458 \| 0.6437 \| \| 0.9513 \| 3.0 \| 2772 \| 0.9737 \| 0.6665 \| \| 0.6634 \| 4.0 \| 3696 \| 1.0015 \| 0.6676 \| \| 0.6268 \| 5.0 \| 4620 \| 0.9964 \| 0.6765 \| \| 0.5239 \| 6.0 \| 5544 \| 1.0639 \| 0.6681 \| \| 0.3603 \| 7.0 \| 6468 \| 1.0932 \| 0.6746 \| \| 0.3057 \| 8.0 \| 7392 \| 1.1360 \| 0.6668 \| \| 0.2497 \| 9.0 \| 8316 \| 1.1511 \| 0.6665 \| \| 0.2212 \| 10.0 \| 9240 \| 1.1733 \| 0.6654 \| ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0 - Datasets 2.14.6 - Tokenizers 0.14.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/swin-large-patch4-window7-224", "model-index": [{"name": "Boya1_RMSProp_1-e5_10Epoch_swin-large-patch4-window7-224_fold1", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.6654002713704206, "name": "Accuracy"}]}]}]}	onizukal/Boya1_RMSProp_1-e5_10Epoch_swin-large-patch4-window7-224_fold1	null	[ "transformers", "safetensors", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/swin-large-patch4-window7-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-26T19:19:17+00:00	[]	[]	TAGS #transformers #safetensors #swin #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/swin-large-patch4-window7-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us	Boya1\_RMSProp\_1-e5\_10Epoch\_swin-large-patch4-window7-224\_fold1 =================================================================== This model is a fine-tuned version of microsoft/swin-large-patch4-window7-224 on the imagefolder dataset. It achieves the following results on the evaluation set: * Loss: 1.1733 * Accuracy: 0.6654 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 1e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_ratio: 0.1 * num\_epochs: 10 ### Training results ### Framework versions * Transformers 4.35.0 * Pytorch 2.1.0 * Datasets 2.14.6 * Tokenizers 0.14.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]	[ "TAGS\n#transformers #safetensors #swin #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/swin-large-patch4-window7-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]
null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	quickstep3621/h3hnzyu	null	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-04-26T19:21:41+00:00	[ "1910.09700" ]	[]	TAGS #transformers #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # GUE_prom_prom_300_notata-seqsight_4096_512_46M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_notata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_notata) dataset. It achieves the following results on the evaluation set: - Loss: 0.1243 - F1 Score: 0.9559 - Accuracy: 0.9559 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.2239 \| 0.6 \| 200 \| 0.1337 \| 0.9497 \| 0.9497 \| \| 0.1349 \| 1.2 \| 400 \| 0.1317 \| 0.9474 \| 0.9474 \| \| 0.1255 \| 1.81 \| 600 \| 0.1126 \| 0.9557 \| 0.9557 \| \| 0.1105 \| 2.41 \| 800 \| 0.1118 \| 0.9557 \| 0.9557 \| \| 0.1119 \| 3.01 \| 1000 \| 0.1097 \| 0.9583 \| 0.9584 \| \| 0.1055 \| 3.61 \| 1200 \| 0.1186 \| 0.9545 \| 0.9546 \| \| 0.1037 \| 4.22 \| 1400 \| 0.1143 \| 0.9576 \| 0.9576 \| \| 0.1021 \| 4.82 \| 1600 \| 0.1028 \| 0.9602 \| 0.9602 \| \| 0.0999 \| 5.42 \| 1800 \| 0.1138 \| 0.9551 \| 0.9552 \| \| 0.0952 \| 6.02 \| 2000 \| 0.0983 \| 0.9634 \| 0.9634 \| \| 0.0971 \| 6.63 \| 2200 \| 0.0973 \| 0.9625 \| 0.9625 \| \| 0.0951 \| 7.23 \| 2400 \| 0.1080 \| 0.9617 \| 0.9617 \| \| 0.0896 \| 7.83 \| 2600 \| 0.1083 \| 0.9600 \| 0.9601 \| \| 0.0926 \| 8.43 \| 2800 \| 0.0990 \| 0.9634 \| 0.9634 \| \| 0.0911 \| 9.04 \| 3000 \| 0.1049 \| 0.9623 \| 0.9623 \| \| 0.0872 \| 9.64 \| 3200 \| 0.0981 \| 0.9653 \| 0.9653 \| \| 0.0843 \| 10.24 \| 3400 \| 0.1062 \| 0.9623 \| 0.9623 \| \| 0.0883 \| 10.84 \| 3600 \| 0.0984 \| 0.9642 \| 0.9642 \| \| 0.0818 \| 11.45 \| 3800 \| 0.0982 \| 0.9640 \| 0.9640 \| \| 0.0845 \| 12.05 \| 4000 \| 0.1020 \| 0.9638 \| 0.9638 \| \| 0.0842 \| 12.65 \| 4200 \| 0.0954 \| 0.9640 \| 0.9640 \| \| 0.0833 \| 13.25 \| 4400 \| 0.1010 \| 0.9644 \| 0.9644 \| \| 0.0788 \| 13.86 \| 4600 \| 0.0963 \| 0.9642 \| 0.9642 \| \| 0.0815 \| 14.46 \| 4800 \| 0.0999 \| 0.9644 \| 0.9644 \| \| 0.0771 \| 15.06 \| 5000 \| 0.0955 \| 0.9655 \| 0.9655 \| \| 0.0772 \| 15.66 \| 5200 \| 0.1040 \| 0.9629 \| 0.9629 \| \| 0.0787 \| 16.27 \| 5400 \| 0.1045 \| 0.9629 \| 0.9629 \| \| 0.0751 \| 16.87 \| 5600 \| 0.0963 \| 0.9651 \| 0.9651 \| \| 0.0735 \| 17.47 \| 5800 \| 0.0947 \| 0.9661 \| 0.9661 \| \| 0.0785 \| 18.07 \| 6000 \| 0.1004 \| 0.9642 \| 0.9642 \| \| 0.0716 \| 18.67 \| 6200 \| 0.1065 \| 0.9642 \| 0.9642 \| \| 0.075 \| 19.28 \| 6400 \| 0.0968 \| 0.9663 \| 0.9663 \| \| 0.0701 \| 19.88 \| 6600 \| 0.1063 \| 0.9646 \| 0.9646 \| \| 0.0736 \| 20.48 \| 6800 \| 0.0987 \| 0.9653 \| 0.9653 \| \| 0.071 \| 21.08 \| 7000 \| 0.0992 \| 0.9665 \| 0.9665 \| \| 0.0708 \| 21.69 \| 7200 \| 0.0985 \| 0.9665 \| 0.9665 \| \| 0.067 \| 22.29 \| 7400 \| 0.1039 \| 0.9659 \| 0.9659 \| \| 0.0701 \| 22.89 \| 7600 \| 0.0980 \| 0.9674 \| 0.9674 \| \| 0.0686 \| 23.49 \| 7800 \| 0.0992 \| 0.9663 \| 0.9663 \| \| 0.0697 \| 24.1 \| 8000 \| 0.0976 \| 0.9657 \| 0.9657 \| \| 0.0674 \| 24.7 \| 8200 \| 0.1086 \| 0.9647 \| 0.9648 \| \| 0.0682 \| 25.3 \| 8400 \| 0.0996 \| 0.9670 \| 0.9670 \| \| 0.0663 \| 25.9 \| 8600 \| 0.0990 \| 0.9670 \| 0.9670 \| \| 0.0663 \| 26.51 \| 8800 \| 0.0998 \| 0.9674 \| 0.9674 \| \| 0.068 \| 27.11 \| 9000 \| 0.0995 \| 0.9665 \| 0.9665 \| \| 0.0644 \| 27.71 \| 9200 \| 0.1019 \| 0.9668 \| 0.9668 \| \| 0.0647 \| 28.31 \| 9400 \| 0.1017 \| 0.9665 \| 0.9665 \| \| 0.0672 \| 28.92 \| 9600 \| 0.1004 \| 0.9666 \| 0.9666 \| \| 0.0625 \| 29.52 \| 9800 \| 0.1013 \| 0.9663 \| 0.9663 \| \| 0.0646 \| 30.12 \| 10000 \| 0.1010 \| 0.9663 \| 0.9663 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_prom_prom_300_notata-seqsight_4096_512_46M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_prom_prom_300_notata-seqsight_4096_512_46M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_46M", "region:us" ]	null	2024-04-26T19:22:01+00:00	[]	[]	TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us	GUE\_prom\_prom\_300\_notata-seqsight\_4096\_512\_46M-L8\_f =========================================================== This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_46M on the mahdibaghbanzadeh/GUE\_prom\_prom\_300\_notata dataset. It achieves the following results on the evaluation set: * Loss: 0.1243 * F1 Score: 0.9559 * Accuracy: 0.9559 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]	[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]
null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	liquid9212/nd2jbc1	null	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-04-26T19:22:22+00:00	[ "1910.09700" ]	[]	TAGS #transformers #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
automatic-speech-recognition	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Cantanese This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 16.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.3020 - Wer: 56.0901 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| 0.0965 \| 1.4174 \| 1000 \| 0.2585 \| 58.6483 \| \| 0.0307 \| 2.8349 \| 2000 \| 0.2716 \| 57.0065 \| \| 0.0035 \| 4.2523 \| 3000 \| 0.2918 \| 56.2810 \| \| 0.001 \| 5.6697 \| 4000 \| 0.3020 \| 56.0901 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"language": ["yue"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_16_0"], "metrics": ["wer"], "base_model": "openai/whisper-small", "model-index": [{"name": "Whisper Small Cantanese", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 16.0", "type": "mozilla-foundation/common_voice_16_0", "config": "yue", "split": "None", "args": "config: yue, split: test"}, "metrics": [{"type": "wer", "value": 56.09011072928599, "name": "Wer"}]}]}]}	Practice-canto/whisper-small-cantonese_26-04-2024-1922	null	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "yue", "dataset:mozilla-foundation/common_voice_16_0", "base_model:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	null	2024-04-26T19:22:26+00:00	[]	[ "yue" ]	TAGS #transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #yue #dataset-mozilla-foundation/common_voice_16_0 #base_model-openai/whisper-small #license-apache-2.0 #model-index #endpoints_compatible #region-us	Whisper Small Cantanese ======================= This model is a fine-tuned version of openai/whisper-small on the Common Voice 16.0 dataset. It achieves the following results on the evaluation set: * Loss: 0.3020 * Wer: 56.0901 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 1e-05 * train\_batch\_size: 4 * eval\_batch\_size: 4 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 8 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 500 * training\_steps: 4000 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.40.1 * Pytorch 2.2.1+cu121 * Datasets 2.19.0 * Tokenizers 0.19.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 4000\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.1\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #yue #dataset-mozilla-foundation/common_voice_16_0 #base_model-openai/whisper-small #license-apache-2.0 #model-index #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 4000\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.1\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]
null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # GUE_prom_prom_300_notata-seqsight_4096_512_46M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_notata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_notata) dataset. It achieves the following results on the evaluation set: - Loss: 0.1358 - F1 Score: 0.9555 - Accuracy: 0.9555 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.2044 \| 0.6 \| 200 \| 0.1225 \| 0.9555 \| 0.9555 \| \| 0.126 \| 1.2 \| 400 \| 0.1318 \| 0.9485 \| 0.9486 \| \| 0.1202 \| 1.81 \| 600 \| 0.1154 \| 0.9561 \| 0.9561 \| \| 0.1036 \| 2.41 \| 800 \| 0.1075 \| 0.9604 \| 0.9604 \| \| 0.1066 \| 3.01 \| 1000 \| 0.1122 \| 0.9608 \| 0.9608 \| \| 0.0987 \| 3.61 \| 1200 \| 0.1206 \| 0.9568 \| 0.9568 \| \| 0.097 \| 4.22 \| 1400 \| 0.1066 \| 0.9612 \| 0.9612 \| \| 0.0938 \| 4.82 \| 1600 \| 0.0984 \| 0.9614 \| 0.9614 \| \| 0.0909 \| 5.42 \| 1800 \| 0.1044 \| 0.9602 \| 0.9602 \| \| 0.0877 \| 6.02 \| 2000 \| 0.0958 \| 0.9631 \| 0.9631 \| \| 0.0886 \| 6.63 \| 2200 \| 0.0928 \| 0.9644 \| 0.9644 \| \| 0.0836 \| 7.23 \| 2400 \| 0.1026 \| 0.9632 \| 0.9633 \| \| 0.0779 \| 7.83 \| 2600 \| 0.1011 \| 0.9642 \| 0.9642 \| \| 0.0797 \| 8.43 \| 2800 \| 0.0986 \| 0.9640 \| 0.9640 \| \| 0.0789 \| 9.04 \| 3000 \| 0.1015 \| 0.9651 \| 0.9651 \| \| 0.0733 \| 9.64 \| 3200 \| 0.0952 \| 0.9668 \| 0.9668 \| \| 0.0714 \| 10.24 \| 3400 \| 0.1080 \| 0.9634 \| 0.9634 \| \| 0.0737 \| 10.84 \| 3600 \| 0.0922 \| 0.9668 \| 0.9668 \| \| 0.0651 \| 11.45 \| 3800 \| 0.0968 \| 0.9659 \| 0.9659 \| \| 0.0676 \| 12.05 \| 4000 \| 0.1078 \| 0.9642 \| 0.9642 \| \| 0.0662 \| 12.65 \| 4200 \| 0.0944 \| 0.9661 \| 0.9661 \| \| 0.0638 \| 13.25 \| 4400 \| 0.1070 \| 0.9655 \| 0.9655 \| \| 0.0614 \| 13.86 \| 4600 \| 0.0990 \| 0.9665 \| 0.9665 \| \| 0.0598 \| 14.46 \| 4800 \| 0.1029 \| 0.9681 \| 0.9682 \| \| 0.0568 \| 15.06 \| 5000 \| 0.1000 \| 0.9661 \| 0.9661 \| \| 0.055 \| 15.66 \| 5200 \| 0.1026 \| 0.9666 \| 0.9666 \| \| 0.0544 \| 16.27 \| 5400 \| 0.1151 \| 0.9653 \| 0.9653 \| \| 0.0525 \| 16.87 \| 5600 \| 0.1003 \| 0.9680 \| 0.9680 \| \| 0.05 \| 17.47 \| 5800 \| 0.1067 \| 0.9659 \| 0.9659 \| \| 0.0527 \| 18.07 \| 6000 \| 0.1135 \| 0.9661 \| 0.9661 \| \| 0.0474 \| 18.67 \| 6200 \| 0.1090 \| 0.9674 \| 0.9674 \| \| 0.0498 \| 19.28 \| 6400 \| 0.1081 \| 0.9665 \| 0.9665 \| \| 0.044 \| 19.88 \| 6600 \| 0.1199 \| 0.9676 \| 0.9676 \| \| 0.0452 \| 20.48 \| 6800 \| 0.1131 \| 0.9663 \| 0.9663 \| \| 0.0453 \| 21.08 \| 7000 \| 0.1190 \| 0.9655 \| 0.9655 \| \| 0.0417 \| 21.69 \| 7200 \| 0.1176 \| 0.9651 \| 0.9651 \| \| 0.0385 \| 22.29 \| 7400 \| 0.1210 \| 0.9665 \| 0.9665 \| \| 0.0401 \| 22.89 \| 7600 \| 0.1178 \| 0.9663 \| 0.9663 \| \| 0.0404 \| 23.49 \| 7800 \| 0.1174 \| 0.9668 \| 0.9668 \| \| 0.038 \| 24.1 \| 8000 \| 0.1168 \| 0.9674 \| 0.9674 \| \| 0.037 \| 24.7 \| 8200 \| 0.1268 \| 0.9668 \| 0.9668 \| \| 0.0369 \| 25.3 \| 8400 \| 0.1228 \| 0.9665 \| 0.9665 \| \| 0.0361 \| 25.9 \| 8600 \| 0.1224 \| 0.9674 \| 0.9674 \| \| 0.0349 \| 26.51 \| 8800 \| 0.1238 \| 0.9665 \| 0.9665 \| \| 0.0358 \| 27.11 \| 9000 \| 0.1228 \| 0.9672 \| 0.9672 \| \| 0.033 \| 27.71 \| 9200 \| 0.1271 \| 0.9678 \| 0.9678 \| \| 0.0338 \| 28.31 \| 9400 \| 0.1239 \| 0.9678 \| 0.9678 \| \| 0.0337 \| 28.92 \| 9600 \| 0.1247 \| 0.9670 \| 0.9670 \| \| 0.033 \| 29.52 \| 9800 \| 0.1249 \| 0.9674 \| 0.9674 \| \| 0.0315 \| 30.12 \| 10000 \| 0.1260 \| 0.9678 \| 0.9678 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_prom_prom_300_notata-seqsight_4096_512_46M-L32_f", "results": []}]}	mahdibaghbanzadeh/GUE_prom_prom_300_notata-seqsight_4096_512_46M-L32_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_46M", "region:us" ]	null	2024-04-26T19:23:23+00:00	[]	[]	TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us	GUE\_prom\_prom\_300\_notata-seqsight\_4096\_512\_46M-L32\_f ============================================================ This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_46M on the mahdibaghbanzadeh/GUE\_prom\_prom\_300\_notata dataset. It achieves the following results on the evaluation set: * Loss: 0.1358 * F1 Score: 0.9555 * Accuracy: 0.9555 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]	[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]
null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # GUE_prom_prom_core_all-seqsight_4096_512_46M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_all](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_all) dataset. It achieves the following results on the evaluation set: - Loss: 0.4112 - F1 Score: 0.8150 - Accuracy: 0.8150 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.5196 \| 0.54 \| 200 \| 0.4708 \| 0.7768 \| 0.7769 \| \| 0.4716 \| 1.08 \| 400 \| 0.4598 \| 0.7880 \| 0.7880 \| \| 0.4517 \| 1.62 \| 600 \| 0.4506 \| 0.7938 \| 0.7939 \| \| 0.4415 \| 2.16 \| 800 \| 0.4477 \| 0.7969 \| 0.7971 \| \| 0.4363 \| 2.7 \| 1000 \| 0.4394 \| 0.8020 \| 0.8020 \| \| 0.4319 \| 3.24 \| 1200 \| 0.4358 \| 0.8047 \| 0.8047 \| \| 0.4314 \| 3.78 \| 1400 \| 0.4343 \| 0.8023 \| 0.8024 \| \| 0.423 \| 4.32 \| 1600 \| 0.4372 \| 0.8047 \| 0.8047 \| \| 0.4253 \| 4.86 \| 1800 \| 0.4352 \| 0.8042 \| 0.8042 \| \| 0.4206 \| 5.41 \| 2000 \| 0.4362 \| 0.8038 \| 0.8039 \| \| 0.4178 \| 5.95 \| 2200 \| 0.4362 \| 0.8031 \| 0.8032 \| \| 0.4225 \| 6.49 \| 2400 \| 0.4350 \| 0.8012 \| 0.8014 \| \| 0.4139 \| 7.03 \| 2600 \| 0.4444 \| 0.7954 \| 0.7959 \| \| 0.4152 \| 7.57 \| 2800 \| 0.4349 \| 0.8046 \| 0.8047 \| \| 0.4106 \| 8.11 \| 3000 \| 0.4325 \| 0.8047 \| 0.8047 \| \| 0.4109 \| 8.65 \| 3200 \| 0.4320 \| 0.8060 \| 0.8061 \| \| 0.4135 \| 9.19 \| 3400 \| 0.4323 \| 0.8050 \| 0.8051 \| \| 0.4087 \| 9.73 \| 3600 \| 0.4303 \| 0.8039 \| 0.8041 \| \| 0.4125 \| 10.27 \| 3800 \| 0.4304 \| 0.8057 \| 0.8057 \| \| 0.4096 \| 10.81 \| 4000 \| 0.4388 \| 0.8005 \| 0.8008 \| \| 0.4056 \| 11.35 \| 4200 \| 0.4316 \| 0.8054 \| 0.8054 \| \| 0.4081 \| 11.89 \| 4400 \| 0.4267 \| 0.8065 \| 0.8066 \| \| 0.4043 \| 12.43 \| 4600 \| 0.4385 \| 0.7998 \| 0.8002 \| \| 0.4051 \| 12.97 \| 4800 \| 0.4293 \| 0.8064 \| 0.8064 \| \| 0.4047 \| 13.51 \| 5000 \| 0.4303 \| 0.8044 \| 0.8046 \| \| 0.4027 \| 14.05 \| 5200 \| 0.4253 \| 0.8059 \| 0.8059 \| \| 0.4024 \| 14.59 \| 5400 \| 0.4291 \| 0.8030 \| 0.8032 \| \| 0.4027 \| 15.14 \| 5600 \| 0.4280 \| 0.8030 \| 0.8032 \| \| 0.4003 \| 15.68 \| 5800 \| 0.4260 \| 0.8074 \| 0.8074 \| \| 0.3969 \| 16.22 \| 6000 \| 0.4248 \| 0.8081 \| 0.8081 \| \| 0.4046 \| 16.76 \| 6200 \| 0.4225 \| 0.8076 \| 0.8076 \| \| 0.4017 \| 17.3 \| 6400 \| 0.4248 \| 0.8069 \| 0.8069 \| \| 0.4005 \| 17.84 \| 6600 \| 0.4247 \| 0.8064 \| 0.8064 \| \| 0.3962 \| 18.38 \| 6800 \| 0.4272 \| 0.8074 \| 0.8076 \| \| 0.3982 \| 18.92 \| 7000 \| 0.4214 \| 0.8094 \| 0.8095 \| \| 0.3917 \| 19.46 \| 7200 \| 0.4235 \| 0.8091 \| 0.8091 \| \| 0.4023 \| 20.0 \| 7400 \| 0.4240 \| 0.8047 \| 0.8047 \| \| 0.3966 \| 20.54 \| 7600 \| 0.4252 \| 0.8074 \| 0.8074 \| \| 0.3947 \| 21.08 \| 7800 \| 0.4236 \| 0.8089 \| 0.8090 \| \| 0.3967 \| 21.62 \| 8000 \| 0.4241 \| 0.8064 \| 0.8064 \| \| 0.3944 \| 22.16 \| 8200 \| 0.4229 \| 0.8078 \| 0.8078 \| \| 0.4017 \| 22.7 \| 8400 \| 0.4208 \| 0.8084 \| 0.8084 \| \| 0.3934 \| 23.24 \| 8600 \| 0.4210 \| 0.8061 \| 0.8061 \| \| 0.3929 \| 23.78 \| 8800 \| 0.4220 \| 0.8078 \| 0.8078 \| \| 0.3926 \| 24.32 \| 9000 \| 0.4224 \| 0.8066 \| 0.8066 \| \| 0.3922 \| 24.86 \| 9200 \| 0.4219 \| 0.8068 \| 0.8068 \| \| 0.4005 \| 25.41 \| 9400 \| 0.4213 \| 0.8093 \| 0.8093 \| \| 0.3897 \| 25.95 \| 9600 \| 0.4218 \| 0.8083 \| 0.8083 \| \| 0.3949 \| 26.49 \| 9800 \| 0.4218 \| 0.8088 \| 0.8088 \| \| 0.3903 \| 27.03 \| 10000 \| 0.4219 \| 0.8089 \| 0.8090 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_prom_prom_core_all-seqsight_4096_512_46M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_prom_prom_core_all-seqsight_4096_512_46M-L1_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_46M", "region:us" ]	null	2024-04-26T19:23:23+00:00	[]	[]	TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us	GUE\_prom\_prom\_core\_all-seqsight\_4096\_512\_46M-L1\_f ========================================================= This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_46M on the mahdibaghbanzadeh/GUE\_prom\_prom\_core\_all dataset. It achieves the following results on the evaluation set: * Loss: 0.4112 * F1 Score: 0.8150 * Accuracy: 0.8150 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]	[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]
null	null	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) saiga_llama3_8b - GGUF - Model creator: https://huggingface.co/IlyaGusev/ - Original model: https://huggingface.co/IlyaGusev/saiga_llama3_8b/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [saiga_llama3_8b.Q2_K.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_llama3_8b-gguf/blob/main/saiga_llama3_8b.Q2_K.gguf) \| Q2_K \| 2.96GB \| \| [saiga_llama3_8b.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_llama3_8b-gguf/blob/main/saiga_llama3_8b.IQ3_XS.gguf) \| IQ3_XS \| 3.28GB \| \| [saiga_llama3_8b.IQ3_S.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_llama3_8b-gguf/blob/main/saiga_llama3_8b.IQ3_S.gguf) \| IQ3_S \| 3.43GB \| \| [saiga_llama3_8b.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_llama3_8b-gguf/blob/main/saiga_llama3_8b.Q3_K_S.gguf) \| Q3_K_S \| 3.41GB \| \| [saiga_llama3_8b.IQ3_M.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_llama3_8b-gguf/blob/main/saiga_llama3_8b.IQ3_M.gguf) \| IQ3_M \| 3.52GB \| \| [saiga_llama3_8b.Q3_K.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_llama3_8b-gguf/blob/main/saiga_llama3_8b.Q3_K.gguf) \| Q3_K \| 3.74GB \| \| [saiga_llama3_8b.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_llama3_8b-gguf/blob/main/saiga_llama3_8b.Q3_K_M.gguf) \| Q3_K_M \| 3.74GB \| \| [saiga_llama3_8b.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_llama3_8b-gguf/blob/main/saiga_llama3_8b.Q3_K_L.gguf) \| Q3_K_L \| 4.03GB \| \| [saiga_llama3_8b.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_llama3_8b-gguf/blob/main/saiga_llama3_8b.IQ4_XS.gguf) \| IQ4_XS \| 4.18GB \| \| [saiga_llama3_8b.Q4_0.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_llama3_8b-gguf/blob/main/saiga_llama3_8b.Q4_0.gguf) \| Q4_0 \| 4.34GB \| \| [saiga_llama3_8b.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_llama3_8b-gguf/blob/main/saiga_llama3_8b.IQ4_NL.gguf) \| IQ4_NL \| 4.38GB \| \| [saiga_llama3_8b.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_llama3_8b-gguf/blob/main/saiga_llama3_8b.Q4_K_S.gguf) \| Q4_K_S \| 4.37GB \| \| [saiga_llama3_8b.Q4_K.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_llama3_8b-gguf/blob/main/saiga_llama3_8b.Q4_K.gguf) \| Q4_K \| 4.58GB \| \| [saiga_llama3_8b.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_llama3_8b-gguf/blob/main/saiga_llama3_8b.Q4_K_M.gguf) \| Q4_K_M \| 4.58GB \| \| [saiga_llama3_8b.Q4_1.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_llama3_8b-gguf/blob/main/saiga_llama3_8b.Q4_1.gguf) \| Q4_1 \| 4.78GB \| \| [saiga_llama3_8b.Q5_0.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_llama3_8b-gguf/blob/main/saiga_llama3_8b.Q5_0.gguf) \| Q5_0 \| 5.21GB \| \| [saiga_llama3_8b.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_llama3_8b-gguf/blob/main/saiga_llama3_8b.Q5_K_S.gguf) \| Q5_K_S \| 5.21GB \| \| [saiga_llama3_8b.Q5_K.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_llama3_8b-gguf/blob/main/saiga_llama3_8b.Q5_K.gguf) \| Q5_K \| 5.34GB \| \| [saiga_llama3_8b.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_llama3_8b-gguf/blob/main/saiga_llama3_8b.Q5_K_M.gguf) \| Q5_K_M \| 5.34GB \| \| [saiga_llama3_8b.Q5_1.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_llama3_8b-gguf/blob/main/saiga_llama3_8b.Q5_1.gguf) \| Q5_1 \| 5.65GB \| \| [saiga_llama3_8b.Q6_K.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_llama3_8b-gguf/blob/main/saiga_llama3_8b.Q6_K.gguf) \| Q6_K \| 6.14GB \| Original model description: --- language: - ru datasets: - IlyaGusev/saiga_scored license: other license_name: llama3 license_link: https://llama.meta.com/llama3/license/ --- # Saiga/Llama3 8B, Russian Llama-3-based chatbot Based on [Llama-3 8B Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct). Llama.cpp version: [link](https://huggingface.co/IlyaGusev/saiga_llama3_8b_gguf) ОСТОРОЖНО! WARNING! LET OP! I've changed the prompt format from ChatML to the original Llama-3 format in v4. Don't forget to switch formats! v4: LLama-3 prompt format: ``` <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> Ты — Сайга, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им.<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> Как дела?<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> Отлично, а у тебя?<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> Шикарно. Как пройти в библиотеку?<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> ``` v2, v3: ChatML prompt format: ``` <\|im_start\|>system Ты — Сайга, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им.<\|im_end\|> <\|im_start\|>user Как дела?<\|im_end\|> <\|im_start\|>assistant Отлично, а у тебя?<\|im_end\|> <\|im_start\|>user Шикарно. Как пройти в библиотеку?<\|im_end\|> <\|im_start\|>assistant ``` Examples: ``` User: Почему трава зеленая? Saiga: Зеленый цвет травы обусловлен наличием хлорофилла, пигмента, который поглощает свет и передает энергию для фотосинтеза. Хлорофилл содержится в клетках листьев растений и играет ключевую роль в процессе фотосинтеза, когда растения используют солнечный свет для превращения воды и углекислого газа в органические вещества, такие как глюкоза и олигоэфиры. Хлорофилл состоит из двух основных форм: хлорофилла а и хлорофилла б. Хлорофилл а поглощает свет с длиной волны около 430 нанометров (нм), что соответствует синему или голубому свету, а хлорофилл б поглощает свет с длиной волны около 660 нм, что соответствует красному свету. Когда свет поглощается хлорофиллом, он передается в молекулы воды и углекислого газа, которые используются для фотосинтеза. Таким образом, хлорофилл является основным компонентом, ответственным за зеленый цвет листьев растений, так как он поглощает свет и передает его энергию для фотосинтеза. ``` ``` User: Сочини длинный рассказ, обязательно упоминая следующие объекты. Дано: Таня, мяч Saiga: Однажды в солнечную погоду на поле для игры в футбол приехала группа друзей, чтобы провести время вместе. В числе их был и Таня, которая была страстной футбольной фанаткой и всегда радовал своих друзей своим энтузиазмом и умением забивать мячи. В этот день, как обычно, друзья решили провести соревнование между собой, чтобы определить кто из них является лучшим футболистом. Таня была очень уверена в своих силах и готовилась к этому матчу с особой серьёзностью. Когда все друзья собрались на поле, они увидели, что перед ними стоял огромный мяч, который должен был стать предметом состязания. Мяч был огромным и тяжелым, и его размеры были необычайно большими по сравнению с обычными мячами, которые используются в футболе. Таня была первая, кто решил начать игру. Она подошла к мячу и начала его удерживать, стараясь выдержать его вес и силу. Но мяч оказался настолько тяжелым, что Таня не смогла удержать его и он упал на землю. Друзья посмеялись над ее неудачей, но Таня не отчаивалась и продолжила пытаться удержать мяч. Она стала использовать все свои силы и умения, чтобы выдержать его вес и силу. Наконец, после долгих усилий, она смогла удержать мяч и начала его бросать в сторону. Мяч летел высоко вверх, и друзья смотрели, как он пролетает над полем. Но мяч неожиданно повернул и стал лететь обратно к Тане. Она успела поймать его и продолжила играть, используя все свои навыки и умения. ``` v4: - [1cc945d4ca2c7901cf989e7edaac52ab24f1a7dd](https://huggingface.co/IlyaGusev/saiga_llama3_8b/commit/1cc945d4ca2c7901cf989e7edaac52ab24f1a7dd) - dataset: [saiga_scored](https://huggingface.co/datasets/IlyaGusev/saiga_scored), scores >= 8, c66032920556c0f21bbbed05e7e04433ec954c3d - wandb [link](https://wandb.ai/ilyagusev/rulm_self_instruct/runs/dcbs9ttt) v3: - [c588356cd60bdee54d52c2dd5a2445acca8aa5c3](https://huggingface.co/IlyaGusev/saiga_llama3_8b/commit/c588356cd60bdee54d52c2dd5a2445acca8aa5c3) - dataset: [saiga_scored](https://huggingface.co/datasets/IlyaGusev/saiga_scored), scores >= 8, d51cf8060bdc90023da8cf1c3f113f9193d6569b - wandb [link](https://wandb.ai/ilyagusev/rulm_self_instruct/runs/ltoqdsal) v2: - [ae61b4f9b34fac9856d361ea78c66284a00e4f0b](https://huggingface.co/IlyaGusev/saiga_llama3_8b/commit/ae61b4f9b34fac9856d361ea78c66284a00e4f0b) - dataset code revision d0d123dd221e10bb2a3383bcb1c6e4efe1b4a28a - wandb [link](https://wandb.ai/ilyagusev/huggingface/runs/r6u5juyk) - 5 datasets: ru_turbo_saiga, ru_sharegpt_cleaned, oasst1_ru_main_branch, gpt_roleplay_realm, ru_instruct_gpt4 - Datasets merging script: [create_short_chat_set.py](https://github.com/IlyaGusev/rulm/blob/d0d123dd221e10bb2a3383bcb1c6e4efe1b4a28a/self_instruct/src/data_processing/create_short_chat_set.py) # Evaluation * Dataset: https://github.com/IlyaGusev/rulm/blob/master/self_instruct/data/tasks.jsonl * Framework: https://github.com/tatsu-lab/alpaca_eval * Evaluator: alpaca_eval_cot_gpt4_turbo_fn \| model \| length_controlled_winrate \| win_rate \| standard_error \| avg_length \| \|-----\|-----\|-----\|-----\|-----\| \|chatgpt_4_turbo \| 76.04 \| 90.00 \|1.46 \| 1270 \| \|chatgpt_3_5_turbo \| 50.00 \| 50.00 \| 0.00 \| 536 \| \|saiga_llama3_8b, v4 \| 43.64 \| 65.90 \| 2.31 \| 1200 \| \|saiga_llama3_8b, v3 \| 36.97 \| 61.08 \| 2.38 \| 1162 \| \|saiga_llama3_8b, v2 \| 33.07 \| 48.19 \| 2.45 \| 1166 \| \|saiga_mistral_7b \| 23.38 \| 35.99 \| 2.34 \| 949 \|	{}	RichardErkhov/IlyaGusev_-_saiga_llama3_8b-gguf	null	[ "gguf", "region:us" ]	null	2024-04-26T19:23:31+00:00	[]	[]	TAGS #gguf #region-us	Quantization made by Richard Erkhov. Github Discord Request more models saiga\_llama3\_8b - GGUF * Model creator: URL * Original model: URL Name: saiga\_llama3\_8b.Q2\_K.gguf, Quant method: Q2\_K, Size: 2.96GB Name: saiga\_llama3\_8b.IQ3\_XS.gguf, Quant method: IQ3\_XS, Size: 3.28GB Name: saiga\_llama3\_8b.IQ3\_S.gguf, Quant method: IQ3\_S, Size: 3.43GB Name: saiga\_llama3\_8b.Q3\_K\_S.gguf, Quant method: Q3\_K\_S, Size: 3.41GB Name: saiga\_llama3\_8b.IQ3\_M.gguf, Quant method: IQ3\_M, Size: 3.52GB Name: saiga\_llama3\_8b.Q3\_K.gguf, Quant method: Q3\_K, Size: 3.74GB Name: saiga\_llama3\_8b.Q3\_K\_M.gguf, Quant method: Q3\_K\_M, Size: 3.74GB Name: saiga\_llama3\_8b.Q3\_K\_L.gguf, Quant method: Q3\_K\_L, Size: 4.03GB Name: saiga\_llama3\_8b.IQ4\_XS.gguf, Quant method: IQ4\_XS, Size: 4.18GB Name: saiga\_llama3\_8b.Q4\_0.gguf, Quant method: Q4\_0, Size: 4.34GB Name: saiga\_llama3\_8b.IQ4\_NL.gguf, Quant method: IQ4\_NL, Size: 4.38GB Name: saiga\_llama3\_8b.Q4\_K\_S.gguf, Quant method: Q4\_K\_S, Size: 4.37GB Name: saiga\_llama3\_8b.Q4\_K.gguf, Quant method: Q4\_K, Size: 4.58GB Name: saiga\_llama3\_8b.Q4\_K\_M.gguf, Quant method: Q4\_K\_M, Size: 4.58GB Name: saiga\_llama3\_8b.Q4\_1.gguf, Quant method: Q4\_1, Size: 4.78GB Name: saiga\_llama3\_8b.Q5\_0.gguf, Quant method: Q5\_0, Size: 5.21GB Name: saiga\_llama3\_8b.Q5\_K\_S.gguf, Quant method: Q5\_K\_S, Size: 5.21GB Name: saiga\_llama3\_8b.Q5\_K.gguf, Quant method: Q5\_K, Size: 5.34GB Name: saiga\_llama3\_8b.Q5\_K\_M.gguf, Quant method: Q5\_K\_M, Size: 5.34GB Name: saiga\_llama3\_8b.Q5\_1.gguf, Quant method: Q5\_1, Size: 5.65GB Name: saiga\_llama3\_8b.Q6\_K.gguf, Quant method: Q6\_K, Size: 6.14GB Original model description: --------------------------- language: * ru datasets: * IlyaGusev/saiga\_scored license: other license\_name: llama3 license\_link: URL --- Saiga/Llama3 8B, Russian Llama-3-based chatbot ============================================== Based on Llama-3 8B Instruct. URL version: link ОСТОРОЖНО! WARNING! LET OP! I've changed the prompt format from ChatML to the original Llama-3 format in v4. Don't forget to switch formats! v4: LLama-3 prompt format: v2, v3: ChatML prompt format: Examples: v4: * 1cc945d4ca2c7901cf989e7edaac52ab24f1a7dd * dataset: saiga\_scored, scores >= 8, c66032920556c0f21bbbed05e7e04433ec954c3d * wandb link v3: * c588356cd60bdee54d52c2dd5a2445acca8aa5c3 * dataset: saiga\_scored, scores >= 8, d51cf8060bdc90023da8cf1c3f113f9193d6569b * wandb link v2: * ae61b4f9b34fac9856d361ea78c66284a00e4f0b * dataset code revision d0d123dd221e10bb2a3383bcb1c6e4efe1b4a28a * wandb link * 5 datasets: ru\_turbo\_saiga, ru\_sharegpt\_cleaned, oasst1\_ru\_main\_branch, gpt\_roleplay\_realm, ru\_instruct\_gpt4 * Datasets merging script: create\_short\_chat\_set.py Evaluation ========== * Dataset: URL * Framework: URL * Evaluator: alpaca\_eval\_cot\_gpt4\_turbo\_fn	[]	[ "TAGS\n#gguf #region-us \n" ]
null	null	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) saiga_gemma_9b - GGUF - Model creator: https://huggingface.co/IlyaGusev/ - Original model: https://huggingface.co/IlyaGusev/saiga_gemma_9b/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [saiga_gemma_9b.Q2_K.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_gemma_9b-gguf/blob/main/saiga_gemma_9b.Q2_K.gguf) \| Q2_K \| 3.24GB \| \| [saiga_gemma_9b.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_gemma_9b-gguf/blob/main/saiga_gemma_9b.IQ3_XS.gguf) \| IQ3_XS \| 3.54GB \| \| [saiga_gemma_9b.IQ3_S.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_gemma_9b-gguf/blob/main/saiga_gemma_9b.IQ3_S.gguf) \| IQ3_S \| 3.71GB \| \| [saiga_gemma_9b.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_gemma_9b-gguf/blob/main/saiga_gemma_9b.Q3_K_S.gguf) \| Q3_K_S \| 3.71GB \| \| [saiga_gemma_9b.IQ3_M.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_gemma_9b-gguf/blob/main/saiga_gemma_9b.IQ3_M.gguf) \| IQ3_M \| 3.82GB \| \| [saiga_gemma_9b.Q3_K.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_gemma_9b-gguf/blob/main/saiga_gemma_9b.Q3_K.gguf) \| Q3_K \| 4.07GB \| \| [saiga_gemma_9b.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_gemma_9b-gguf/blob/main/saiga_gemma_9b.Q3_K_M.gguf) \| Q3_K_M \| 4.07GB \| \| [saiga_gemma_9b.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_gemma_9b-gguf/blob/main/saiga_gemma_9b.Q3_K_L.gguf) \| Q3_K_L \| 4.39GB \| \| [saiga_gemma_9b.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_gemma_9b-gguf/blob/main/saiga_gemma_9b.IQ4_XS.gguf) \| IQ4_XS \| 4.48GB \| \| [saiga_gemma_9b.Q4_0.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_gemma_9b-gguf/blob/main/saiga_gemma_9b.Q4_0.gguf) \| Q4_0 \| 4.67GB \| \| [saiga_gemma_9b.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_gemma_9b-gguf/blob/main/saiga_gemma_9b.IQ4_NL.gguf) \| IQ4_NL \| 4.69GB \| \| [saiga_gemma_9b.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_gemma_9b-gguf/blob/main/saiga_gemma_9b.Q4_K_S.gguf) \| Q4_K_S \| 4.7GB \| \| [saiga_gemma_9b.Q4_K.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_gemma_9b-gguf/blob/main/saiga_gemma_9b.Q4_K.gguf) \| Q4_K \| 4.96GB \| \| [saiga_gemma_9b.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_gemma_9b-gguf/blob/main/saiga_gemma_9b.Q4_K_M.gguf) \| Q4_K_M \| 4.96GB \| \| [saiga_gemma_9b.Q4_1.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_gemma_9b-gguf/blob/main/saiga_gemma_9b.Q4_1.gguf) \| Q4_1 \| 5.12GB \| \| [saiga_gemma_9b.Q5_0.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_gemma_9b-gguf/blob/main/saiga_gemma_9b.Q5_0.gguf) \| Q5_0 \| 5.57GB \| \| [saiga_gemma_9b.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_gemma_9b-gguf/blob/main/saiga_gemma_9b.Q5_K_S.gguf) \| Q5_K_S \| 5.57GB \| \| [saiga_gemma_9b.Q5_K.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_gemma_9b-gguf/blob/main/saiga_gemma_9b.Q5_K.gguf) \| Q5_K \| 5.72GB \| \| [saiga_gemma_9b.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_gemma_9b-gguf/blob/main/saiga_gemma_9b.Q5_K_M.gguf) \| Q5_K_M \| 5.72GB \| \| [saiga_gemma_9b.Q5_1.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_gemma_9b-gguf/blob/main/saiga_gemma_9b.Q5_1.gguf) \| Q5_1 \| 6.02GB \| \| [saiga_gemma_9b.Q6_K.gguf](https://huggingface.co/RichardErkhov/IlyaGusev_-_saiga_gemma_9b-gguf/blob/main/saiga_gemma_9b.Q6_K.gguf) \| Q6_K \| 6.53GB \| Original model description: --- language: - ru datasets: - IlyaGusev/ru_turbo_saiga - IlyaGusev/ru_sharegpt_cleaned - IlyaGusev/oasst1_ru_main_branch - IlyaGusev/gpt_roleplay_realm - lksy/ru_instruct_gpt4 --- # Saiga/Gemma 9B, Russian Gemma-based chatbot Based on [Gemma 7B](https://huggingface.co/google/gemma-7b). Training Colab: [link](https://colab.research.google.com/drive/1O7F7Q3IQYh-v7EfsdRwMDMfUnOYZ3DEh). ChatML prompt format: ``` <\|im_start\|>system Ты — Сайга, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им.<\|im_end\|> <\|im_start\|>user Привет!<\|im_end\|> <\|im_start\|>assistant Отлично, а ты?<\|im_end\|> <\|im_start\|>user Шикарно. Как пройти в библиотеку?<\|im_end\|> <\|im_start\|>assistant ``` Examples: ``` User: Почему трава зеленая? Saiga: Зеленый цвет травы обусловлен наличием в её клетках пигмента хлорофилла, который играет важную роль в процессе фотосинтеза – преобразования солнечного света в энергию, используемую для роста и развития растений. Хлорофилл поглощает свет в определенных диапазонах длин волн, в том числе в зеленой области спектра, и отражает свет в других диапазонах, что и обуславливает зелёный цвет. ``` ``` User: Сочини длинный рассказ, обязательно упоминая следующие объекты. Дано: Таня, мяч Saiga: Однажды, в далекой стране, жила маленькая девочка по имени Таня. Она была очень активной и любила играть в мяч. Вместе со своими друзьями они проводили дни, играя в футбол, баскетбол и волейбол. Таня была очень талантливой и ловкой, и она часто выигрывала в играх с другими детьми. Но однажды, во время игры в баскетбол, она получила сильную травму. Ей пришлось провести несколько недель в больнице, и она была очень грустной и унылой. Её друзья не могли понять, как помочь Тане справиться с этой ситуацией. Они пытались поддерживать её, но Таня была слишком подавлена и не хотела играть в мяч. Однажды, Таня увидела, как её друзья играют в мяч. Она не могла не смотреть, как они ловко передают мяч друг другу и забивают голы. Таня почувствовала, как её сердце разрывается от желания играть вместе с ними. Она решила, что ей нужно вернуться в игру, чтобы снова почувствовать радость игры. Она начала медленно восстанавливаться и постепенно возвращалась к своему любимому занятию - игре в мяч. Таня стала играть в мяч снова, но теперь она была более осторожна и внимательна к своему здоровью. Её друзья были очень рады видеть, как Таня возвращается к своей привычной жизни. С тех пор, Таня стала настоящей звездой на поле. Она стала лучшей игроком в своей команде и помогла им выиграть множество игр. Её друзья были горды, что Таня вернулась к своему любимому занятию и снова стала такой же активной и ловкой, как раньше. Таня и её друзья продолжали играть в мяч и проводили чудесные дни вместе. Они были счастливы, что Таня вернулась к своему любимому занятию и снова стала частью команды. Таня и её друзья продолжают играть в мяч и вместе они создают прекрасные воспоминания. ``` v1: - dataset code revision d0d123dd221e10bb2a3383bcb1c6e4efe1b4a28a - wandb [link](https://wandb.ai/ilyagusev/gemma_test/runs/k7u3uw5i) - 5 datasets: ru_turbo_saiga, ru_sharegpt_cleaned, oasst1_ru_main_branch, gpt_roleplay_realm, ru_instruct_gpt4 - Datasets merging script: [create_short_chat_set.py](https://github.com/IlyaGusev/rulm/blob/d0d123dd221e10bb2a3383bcb1c6e4efe1b4a28a/self_instruct/src/data_processing/create_short_chat_set.py)	{}	RichardErkhov/IlyaGusev_-_saiga_gemma_9b-gguf	null	[ "gguf", "region:us" ]	null	2024-04-26T19:25:57+00:00	[]	[]	TAGS #gguf #region-us	Quantization made by Richard Erkhov. Github Discord Request more models saiga\_gemma\_9b - GGUF * Model creator: URL * Original model: URL Name: saiga\_gemma\_9b.Q2\_K.gguf, Quant method: Q2\_K, Size: 3.24GB Name: saiga\_gemma\_9b.IQ3\_XS.gguf, Quant method: IQ3\_XS, Size: 3.54GB Name: saiga\_gemma\_9b.IQ3\_S.gguf, Quant method: IQ3\_S, Size: 3.71GB Name: saiga\_gemma\_9b.Q3\_K\_S.gguf, Quant method: Q3\_K\_S, Size: 3.71GB Name: saiga\_gemma\_9b.IQ3\_M.gguf, Quant method: IQ3\_M, Size: 3.82GB Name: saiga\_gemma\_9b.Q3\_K.gguf, Quant method: Q3\_K, Size: 4.07GB Name: saiga\_gemma\_9b.Q3\_K\_M.gguf, Quant method: Q3\_K\_M, Size: 4.07GB Name: saiga\_gemma\_9b.Q3\_K\_L.gguf, Quant method: Q3\_K\_L, Size: 4.39GB Name: saiga\_gemma\_9b.IQ4\_XS.gguf, Quant method: IQ4\_XS, Size: 4.48GB Name: saiga\_gemma\_9b.Q4\_0.gguf, Quant method: Q4\_0, Size: 4.67GB Name: saiga\_gemma\_9b.IQ4\_NL.gguf, Quant method: IQ4\_NL, Size: 4.69GB Name: saiga\_gemma\_9b.Q4\_K\_S.gguf, Quant method: Q4\_K\_S, Size: 4.7GB Name: saiga\_gemma\_9b.Q4\_K.gguf, Quant method: Q4\_K, Size: 4.96GB Name: saiga\_gemma\_9b.Q4\_K\_M.gguf, Quant method: Q4\_K\_M, Size: 4.96GB Name: saiga\_gemma\_9b.Q4\_1.gguf, Quant method: Q4\_1, Size: 5.12GB Name: saiga\_gemma\_9b.Q5\_0.gguf, Quant method: Q5\_0, Size: 5.57GB Name: saiga\_gemma\_9b.Q5\_K\_S.gguf, Quant method: Q5\_K\_S, Size: 5.57GB Name: saiga\_gemma\_9b.Q5\_K.gguf, Quant method: Q5\_K, Size: 5.72GB Name: saiga\_gemma\_9b.Q5\_K\_M.gguf, Quant method: Q5\_K\_M, Size: 5.72GB Name: saiga\_gemma\_9b.Q5\_1.gguf, Quant method: Q5\_1, Size: 6.02GB Name: saiga\_gemma\_9b.Q6\_K.gguf, Quant method: Q6\_K, Size: 6.53GB Original model description: --------------------------- language: * ru datasets: * IlyaGusev/ru\_turbo\_saiga * IlyaGusev/ru\_sharegpt\_cleaned * IlyaGusev/oasst1\_ru\_main\_branch * IlyaGusev/gpt\_roleplay\_realm * lksy/ru\_instruct\_gpt4 --- Saiga/Gemma 9B, Russian Gemma-based chatbot =========================================== Based on Gemma 7B. Training Colab: link. ChatML prompt format: Examples: v1: * dataset code revision d0d123dd221e10bb2a3383bcb1c6e4efe1b4a28a * wandb link * 5 datasets: ru\_turbo\_saiga, ru\_sharegpt\_cleaned, oasst1\_ru\_main\_branch, gpt\_roleplay\_realm, ru\_instruct\_gpt4 * Datasets merging script: create\_short\_chat\_set.py	[]	[ "TAGS\n#gguf #region-us \n" ]
null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # GUE_prom_prom_core_all-seqsight_4096_512_46M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_all](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_all) dataset. It achieves the following results on the evaluation set: - Loss: 0.4053 - F1 Score: 0.8177 - Accuracy: 0.8177 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.4984 \| 0.54 \| 200 \| 0.4572 \| 0.7900 \| 0.7900 \| \| 0.4522 \| 1.08 \| 400 \| 0.4546 \| 0.7940 \| 0.7943 \| \| 0.433 \| 1.62 \| 600 \| 0.4402 \| 0.7991 \| 0.7992 \| \| 0.4282 \| 2.16 \| 800 \| 0.4399 \| 0.8021 \| 0.8022 \| \| 0.4224 \| 2.7 \| 1000 \| 0.4331 \| 0.8047 \| 0.8047 \| \| 0.4188 \| 3.24 \| 1200 \| 0.4272 \| 0.8059 \| 0.8059 \| \| 0.4157 \| 3.78 \| 1400 \| 0.4306 \| 0.8033 \| 0.8034 \| \| 0.4083 \| 4.32 \| 1600 \| 0.4295 \| 0.8057 \| 0.8057 \| \| 0.4128 \| 4.86 \| 1800 \| 0.4294 \| 0.8044 \| 0.8044 \| \| 0.405 \| 5.41 \| 2000 \| 0.4283 \| 0.8038 \| 0.8039 \| \| 0.4044 \| 5.95 \| 2200 \| 0.4296 \| 0.8066 \| 0.8066 \| \| 0.4066 \| 6.49 \| 2400 \| 0.4275 \| 0.8059 \| 0.8059 \| \| 0.3998 \| 7.03 \| 2600 \| 0.4304 \| 0.8066 \| 0.8068 \| \| 0.399 \| 7.57 \| 2800 \| 0.4296 \| 0.8057 \| 0.8057 \| \| 0.3953 \| 8.11 \| 3000 \| 0.4262 \| 0.8102 \| 0.8103 \| \| 0.3932 \| 8.65 \| 3200 \| 0.4228 \| 0.8074 \| 0.8074 \| \| 0.3965 \| 9.19 \| 3400 \| 0.4271 \| 0.8083 \| 0.8083 \| \| 0.3927 \| 9.73 \| 3600 \| 0.4225 \| 0.8090 \| 0.8091 \| \| 0.3945 \| 10.27 \| 3800 \| 0.4243 \| 0.8096 \| 0.8096 \| \| 0.3903 \| 10.81 \| 4000 \| 0.4342 \| 0.8025 \| 0.8027 \| \| 0.3864 \| 11.35 \| 4200 \| 0.4280 \| 0.8092 \| 0.8093 \| \| 0.3885 \| 11.89 \| 4400 \| 0.4219 \| 0.8126 \| 0.8127 \| \| 0.3863 \| 12.43 \| 4600 \| 0.4343 \| 0.8074 \| 0.8076 \| \| 0.3847 \| 12.97 \| 4800 \| 0.4253 \| 0.8105 \| 0.8105 \| \| 0.3824 \| 13.51 \| 5000 \| 0.4253 \| 0.8108 \| 0.8110 \| \| 0.3834 \| 14.05 \| 5200 \| 0.4210 \| 0.8111 \| 0.8111 \| \| 0.3793 \| 14.59 \| 5400 \| 0.4262 \| 0.8096 \| 0.8096 \| \| 0.3813 \| 15.14 \| 5600 \| 0.4269 \| 0.8087 \| 0.8088 \| \| 0.3781 \| 15.68 \| 5800 \| 0.4244 \| 0.8111 \| 0.8111 \| \| 0.3746 \| 16.22 \| 6000 \| 0.4248 \| 0.8103 \| 0.8105 \| \| 0.3796 \| 16.76 \| 6200 \| 0.4194 \| 0.8147 \| 0.8147 \| \| 0.3778 \| 17.3 \| 6400 \| 0.4247 \| 0.8135 \| 0.8135 \| \| 0.3763 \| 17.84 \| 6600 \| 0.4247 \| 0.8137 \| 0.8137 \| \| 0.3707 \| 18.38 \| 6800 \| 0.4286 \| 0.8093 \| 0.8096 \| \| 0.3751 \| 18.92 \| 7000 \| 0.4188 \| 0.8119 \| 0.8120 \| \| 0.3667 \| 19.46 \| 7200 \| 0.4234 \| 0.8130 \| 0.8130 \| \| 0.3777 \| 20.0 \| 7400 \| 0.4236 \| 0.8130 \| 0.8130 \| \| 0.3697 \| 20.54 \| 7600 \| 0.4249 \| 0.8133 \| 0.8133 \| \| 0.3703 \| 21.08 \| 7800 \| 0.4250 \| 0.8133 \| 0.8133 \| \| 0.3693 \| 21.62 \| 8000 \| 0.4215 \| 0.8128 \| 0.8128 \| \| 0.3679 \| 22.16 \| 8200 \| 0.4198 \| 0.8110 \| 0.8110 \| \| 0.3734 \| 22.7 \| 8400 \| 0.4200 \| 0.8123 \| 0.8123 \| \| 0.3659 \| 23.24 \| 8600 \| 0.4188 \| 0.8127 \| 0.8127 \| \| 0.3653 \| 23.78 \| 8800 \| 0.4210 \| 0.8131 \| 0.8132 \| \| 0.3674 \| 24.32 \| 9000 \| 0.4202 \| 0.8137 \| 0.8137 \| \| 0.3646 \| 24.86 \| 9200 \| 0.4201 \| 0.8139 \| 0.8139 \| \| 0.3721 \| 25.41 \| 9400 \| 0.4193 \| 0.8154 \| 0.8154 \| \| 0.3613 \| 25.95 \| 9600 \| 0.4210 \| 0.8150 \| 0.8150 \| \| 0.3673 \| 26.49 \| 9800 \| 0.4205 \| 0.8150 \| 0.8150 \| \| 0.361 \| 27.03 \| 10000 \| 0.4209 \| 0.8140 \| 0.8140 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_prom_prom_core_all-seqsight_4096_512_46M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_prom_prom_core_all-seqsight_4096_512_46M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_46M", "region:us" ]	null	2024-04-26T19:28:24+00:00	[]	[]	TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us	GUE\_prom\_prom\_core\_all-seqsight\_4096\_512\_46M-L8\_f ========================================================= This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_46M on the mahdibaghbanzadeh/GUE\_prom\_prom\_core\_all dataset. It achieves the following results on the evaluation set: * Loss: 0.4053 * F1 Score: 0.8177 * Accuracy: 0.8177 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]	[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]
null	null	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Starling-LM-7B-beta - GGUF - Model creator: https://huggingface.co/Nexusflow/ - Original model: https://huggingface.co/Nexusflow/Starling-LM-7B-beta/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Starling-LM-7B-beta.Q2_K.gguf](https://huggingface.co/RichardErkhov/Nexusflow_-_Starling-LM-7B-beta-gguf/blob/main/Starling-LM-7B-beta.Q2_K.gguf) \| Q2_K \| 2.53GB \| \| [Starling-LM-7B-beta.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Nexusflow_-_Starling-LM-7B-beta-gguf/blob/main/Starling-LM-7B-beta.IQ3_XS.gguf) \| IQ3_XS \| 2.81GB \| \| [Starling-LM-7B-beta.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Nexusflow_-_Starling-LM-7B-beta-gguf/blob/main/Starling-LM-7B-beta.IQ3_S.gguf) \| IQ3_S \| 2.96GB \| \| [Starling-LM-7B-beta.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Nexusflow_-_Starling-LM-7B-beta-gguf/blob/main/Starling-LM-7B-beta.Q3_K_S.gguf) \| Q3_K_S \| 2.95GB \| \| [Starling-LM-7B-beta.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Nexusflow_-_Starling-LM-7B-beta-gguf/blob/main/Starling-LM-7B-beta.IQ3_M.gguf) \| IQ3_M \| 3.06GB \| \| [Starling-LM-7B-beta.Q3_K.gguf](https://huggingface.co/RichardErkhov/Nexusflow_-_Starling-LM-7B-beta-gguf/blob/main/Starling-LM-7B-beta.Q3_K.gguf) \| Q3_K \| 3.28GB \| \| [Starling-LM-7B-beta.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Nexusflow_-_Starling-LM-7B-beta-gguf/blob/main/Starling-LM-7B-beta.Q3_K_M.gguf) \| Q3_K_M \| 3.28GB \| \| [Starling-LM-7B-beta.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Nexusflow_-_Starling-LM-7B-beta-gguf/blob/main/Starling-LM-7B-beta.Q3_K_L.gguf) \| Q3_K_L \| 3.56GB \| \| [Starling-LM-7B-beta.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Nexusflow_-_Starling-LM-7B-beta-gguf/blob/main/Starling-LM-7B-beta.IQ4_XS.gguf) \| IQ4_XS \| 3.67GB \| \| [Starling-LM-7B-beta.Q4_0.gguf](https://huggingface.co/RichardErkhov/Nexusflow_-_Starling-LM-7B-beta-gguf/blob/main/Starling-LM-7B-beta.Q4_0.gguf) \| Q4_0 \| 3.83GB \| \| [Starling-LM-7B-beta.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Nexusflow_-_Starling-LM-7B-beta-gguf/blob/main/Starling-LM-7B-beta.IQ4_NL.gguf) \| IQ4_NL \| 3.87GB \| \| [Starling-LM-7B-beta.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Nexusflow_-_Starling-LM-7B-beta-gguf/blob/main/Starling-LM-7B-beta.Q4_K_S.gguf) \| Q4_K_S \| 3.86GB \| \| [Starling-LM-7B-beta.Q4_K.gguf](https://huggingface.co/RichardErkhov/Nexusflow_-_Starling-LM-7B-beta-gguf/blob/main/Starling-LM-7B-beta.Q4_K.gguf) \| Q4_K \| 4.07GB \| \| [Starling-LM-7B-beta.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Nexusflow_-_Starling-LM-7B-beta-gguf/blob/main/Starling-LM-7B-beta.Q4_K_M.gguf) \| Q4_K_M \| 4.07GB \| \| [Starling-LM-7B-beta.Q4_1.gguf](https://huggingface.co/RichardErkhov/Nexusflow_-_Starling-LM-7B-beta-gguf/blob/main/Starling-LM-7B-beta.Q4_1.gguf) \| Q4_1 \| 4.24GB \| \| [Starling-LM-7B-beta.Q5_0.gguf](https://huggingface.co/RichardErkhov/Nexusflow_-_Starling-LM-7B-beta-gguf/blob/main/Starling-LM-7B-beta.Q5_0.gguf) \| Q5_0 \| 4.65GB \| \| [Starling-LM-7B-beta.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Nexusflow_-_Starling-LM-7B-beta-gguf/blob/main/Starling-LM-7B-beta.Q5_K_S.gguf) \| Q5_K_S \| 4.65GB \| \| [Starling-LM-7B-beta.Q5_K.gguf](https://huggingface.co/RichardErkhov/Nexusflow_-_Starling-LM-7B-beta-gguf/blob/main/Starling-LM-7B-beta.Q5_K.gguf) \| Q5_K \| 4.78GB \| \| [Starling-LM-7B-beta.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Nexusflow_-_Starling-LM-7B-beta-gguf/blob/main/Starling-LM-7B-beta.Q5_K_M.gguf) \| Q5_K_M \| 4.78GB \| \| [Starling-LM-7B-beta.Q5_1.gguf](https://huggingface.co/RichardErkhov/Nexusflow_-_Starling-LM-7B-beta-gguf/blob/main/Starling-LM-7B-beta.Q5_1.gguf) \| Q5_1 \| 5.07GB \| \| [Starling-LM-7B-beta.Q6_K.gguf](https://huggingface.co/RichardErkhov/Nexusflow_-_Starling-LM-7B-beta-gguf/blob/main/Starling-LM-7B-beta.Q6_K.gguf) \| Q6_K \| 5.53GB \| Original model description: --- license: apache-2.0 datasets: - berkeley-nest/Nectar language: - en library_name: transformers tags: - reward model - RLHF - RLAIF --- # Starling-LM-7B-beta <!-- Provide a quick summary of what the model is/does. --> - Developed by: The Nexusflow Team ( Banghua Zhu * , Evan Frick * , Tianhao Wu * , Hanlin Zhu, Karthik Ganesan, Wei-Lin Chiang, Jian Zhang, and Jiantao Jiao). - Model type: Language Model finetuned with RLHF / RLAIF - License: Apache-2.0 license under the condition that the model is not used to compete with OpenAI - Finetuned from model: [Openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106) (based on [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)) We introduce Starling-LM-7B-beta, an open large language model (LLM) trained by Reinforcement Learning from AI Feedback (RLAIF). Starling-LM-7B-beta is trained from [Openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106) with our new reward model [Nexusflow/Starling-RM-34B](https://huggingface.co/Nexusflow/Starling-RM-34B) and policy optimization method [Fine-Tuning Language Models from Human Preferences (PPO)](https://arxiv.org/abs/1909.08593). Harnessing the power of the ranking dataset, [berkeley-nest/Nectar](https://huggingface.co/datasets/berkeley-nest/Nectar), the upgraded reward model, [Starling-RM-34B](https://huggingface.co/Nexusflow/Starling-RM-34B), and the new reward training and policy tuning pipeline, Starling-LM-7B-beta scores an improved 8.12 in MT Bench with GPT-4 as a judge. ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> Important: Please use the exact chat template provided below for the model. Otherwise there will be a degradation in the performance. The model output can be verbose in rare cases. Please consider setting temperature = 0 to make this happen less. Our model follows the exact chat template and usage as [Openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106). Please refer to their model card for more details. In addition, our model is hosted on LMSYS [Chatbot Arena](https://chat.lmsys.org) for free test. The conversation template is the same as Openchat-3.5-0106: ``` import transformers tokenizer = transformers.AutoTokenizer.from_pretrained("openchat/openchat-3.5-0106") # Single-turn tokens = tokenizer("GPT4 Correct User: Hello<\|end_of_turn\|>GPT4 Correct Assistant:").input_ids assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] # Multi-turn tokens = tokenizer("GPT4 Correct User: Hello<\|end_of_turn\|>GPT4 Correct Assistant: Hi<\|end_of_turn\|>GPT4 Correct User: How are you today?<\|end_of_turn\|>GPT4 Correct Assistant:").input_ids assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] # Coding Mode tokens = tokenizer("Code User: Implement quicksort using C++<\|end_of_turn\|>Code Assistant:").input_ids assert tokens == [1, 7596, 1247, 28747, 26256, 2936, 7653, 1413, 334, 1680, 32000, 7596, 21631, 28747] ``` ## Code Examples ```python import transformers tokenizer = transformers.AutoTokenizer.from_pretrained("Nexusflow/Starling-LM-7B-beta") model = transformers.AutoModelForCausalLM.from_pretrained("Nexusflow/Starling-LM-7B-beta") def generate_response(prompt): input_ids = tokenizer(prompt, return_tensors="pt").input_ids outputs = model.generate( input_ids, max_length=256, pad_token_id=tokenizer.pad_token_id, eos_token_id=tokenizer.eos_token_id, ) response_ids = outputs[0] response_text = tokenizer.decode(response_ids, skip_special_tokens=True) return response_text # Single-turn conversation prompt = "Hello, how are you?" single_turn_prompt = f"GPT4 Correct User: {prompt}<\|end_of_turn\|>GPT4 Correct Assistant:" response_text = generate_response(single_turn_prompt) print("Response:", response_text) ## Multi-turn conversation prompt = "Hello" follow_up_question = "How are you today?" response = "" multi_turn_prompt = f"GPT4 Correct User: {prompt}<\|end_of_turn\|>GPT4 Correct Assistant: {response}<\|end_of_turn\|>GPT4 Correct User: {follow_up_question}<\|end_of_turn\|>GPT4 Correct Assistant:" response_text = generate_response(multi_turn_prompt) print("Multi-turn conversation response:", response_text) ### Coding conversation prompt = "Implement quicksort using C++" coding_prompt = f"Code User: {prompt}<\|end_of_turn\|>Code Assistant:" response = generate_response(coding_prompt) print("Coding conversation response:", response) ``` ## License The dataset, model and online demo is subject to the [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI, and [Privacy Practices](https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb) of ShareGPT. Please contact us if you find any potential violation. ## Acknowledgment We would like to thank Tianle Li from UC Berkeley for detailed feedback and evaluation of this beta release. We would like to thank the [LMSYS Organization](https://lmsys.org/) for their support of [lmsys-chat-1M](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) dataset, evaluation and online demo. We would like to thank the open source community for their efforts in providing the datasets and base models we used to develope the project, including but not limited to Anthropic, Llama, Mistral, Hugging Face H4, LMSYS, OpenChat, OpenBMB, Flan and ShareGPT. ## Citation ``` @misc{starling2023, title = {Starling-7B: Improving LLM Helpfulness & Harmlessness with RLAIF}, url = {}, author = {Zhu, Banghua and Frick, Evan and Wu, Tianhao and Zhu, Hanlin and Ganesan, Karthik and Chiang, Wei-Lin and Zhang, Jian and Jiao, Jiantao}, month = {November}, year = {2023} } ```	{}	RichardErkhov/Nexusflow_-_Starling-LM-7B-beta-gguf	null	[ "gguf", "arxiv:1909.08593", "region:us" ]	null	2024-04-26T19:34:23+00:00	[ "1909.08593" ]	[]	TAGS #gguf #arxiv-1909.08593 #region-us	Quantization made by Richard Erkhov. Github Discord Request more models Starling-LM-7B-beta - GGUF * Model creator: URL * Original model: URL Name: Starling-LM-7B-beta.Q2\_K.gguf, Quant method: Q2\_K, Size: 2.53GB Name: Starling-LM-7B-beta.IQ3\_XS.gguf, Quant method: IQ3\_XS, Size: 2.81GB Name: Starling-LM-7B-beta.IQ3\_S.gguf, Quant method: IQ3\_S, Size: 2.96GB Name: Starling-LM-7B-beta.Q3\_K\_S.gguf, Quant method: Q3\_K\_S, Size: 2.95GB Name: Starling-LM-7B-beta.IQ3\_M.gguf, Quant method: IQ3\_M, Size: 3.06GB Name: Starling-LM-7B-beta.Q3\_K.gguf, Quant method: Q3\_K, Size: 3.28GB Name: Starling-LM-7B-beta.Q3\_K\_M.gguf, Quant method: Q3\_K\_M, Size: 3.28GB Name: Starling-LM-7B-beta.Q3\_K\_L.gguf, Quant method: Q3\_K\_L, Size: 3.56GB Name: Starling-LM-7B-beta.IQ4\_XS.gguf, Quant method: IQ4\_XS, Size: 3.67GB Name: Starling-LM-7B-beta.Q4\_0.gguf, Quant method: Q4\_0, Size: 3.83GB Name: Starling-LM-7B-beta.IQ4\_NL.gguf, Quant method: IQ4\_NL, Size: 3.87GB Name: Starling-LM-7B-beta.Q4\_K\_S.gguf, Quant method: Q4\_K\_S, Size: 3.86GB Name: Starling-LM-7B-beta.Q4\_K.gguf, Quant method: Q4\_K, Size: 4.07GB Name: Starling-LM-7B-beta.Q4\_K\_M.gguf, Quant method: Q4\_K\_M, Size: 4.07GB Name: Starling-LM-7B-beta.Q4\_1.gguf, Quant method: Q4\_1, Size: 4.24GB Name: Starling-LM-7B-beta.Q5\_0.gguf, Quant method: Q5\_0, Size: 4.65GB Name: Starling-LM-7B-beta.Q5\_K\_S.gguf, Quant method: Q5\_K\_S, Size: 4.65GB Name: Starling-LM-7B-beta.Q5\_K.gguf, Quant method: Q5\_K, Size: 4.78GB Name: Starling-LM-7B-beta.Q5\_K\_M.gguf, Quant method: Q5\_K\_M, Size: 4.78GB Name: Starling-LM-7B-beta.Q5\_1.gguf, Quant method: Q5\_1, Size: 5.07GB Name: Starling-LM-7B-beta.Q6\_K.gguf, Quant method: Q6\_K, Size: 5.53GB Original model description: --------------------------- license: apache-2.0 datasets: * berkeley-nest/Nectar language: * en library\_name: transformers tags: * reward model * RLHF * RLAIF --- Starling-LM-7B-beta =================== * Developed by: The Nexusflow Team ( Banghua Zhu \* , Evan Frick \* , Tianhao Wu \* , Hanlin Zhu, Karthik Ganesan, Wei-Lin Chiang, Jian Zhang, and Jiantao Jiao). * Model type: Language Model finetuned with RLHF / RLAIF * License: Apache-2.0 license under the condition that the model is not used to compete with OpenAI * Finetuned from model: Openchat-3.5-0106 (based on Mistral-7B-v0.1) We introduce Starling-LM-7B-beta, an open large language model (LLM) trained by Reinforcement Learning from AI Feedback (RLAIF). Starling-LM-7B-beta is trained from Openchat-3.5-0106 with our new reward model Nexusflow/Starling-RM-34B and policy optimization method Fine-Tuning Language Models from Human Preferences (PPO). Harnessing the power of the ranking dataset, berkeley-nest/Nectar, the upgraded reward model, Starling-RM-34B, and the new reward training and policy tuning pipeline, Starling-LM-7B-beta scores an improved 8.12 in MT Bench with GPT-4 as a judge. Uses ---- Important: Please use the exact chat template provided below for the model. Otherwise there will be a degradation in the performance. The model output can be verbose in rare cases. Please consider setting temperature = 0 to make this happen less. Our model follows the exact chat template and usage as Openchat-3.5-0106. Please refer to their model card for more details. In addition, our model is hosted on LMSYS Chatbot Arena for free test. The conversation template is the same as Openchat-3.5-0106: Code Examples ------------- License ------- The dataset, model and online demo is subject to the Terms of Use of the data generated by OpenAI, and Privacy Practices of ShareGPT. Please contact us if you find any potential violation. Acknowledgment -------------- We would like to thank Tianle Li from UC Berkeley for detailed feedback and evaluation of this beta release. We would like to thank the LMSYS Organization for their support of lmsys-chat-1M dataset, evaluation and online demo. We would like to thank the open source community for their efforts in providing the datasets and base models we used to develope the project, including but not limited to Anthropic, Llama, Mistral, Hugging Face H4, LMSYS, OpenChat, OpenBMB, Flan and ShareGPT.	[]	[ "TAGS\n#gguf #arxiv-1909.08593 #region-us \n" ]
text-classification	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	thorirhrafn/gpt1B_domarsft_merged	null	[ "transformers", "safetensors", "gpt2", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-26T19:34:24+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #gpt2 #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. 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This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #gpt2 #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # GUE_prom_prom_core_all-seqsight_4096_512_46M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_all](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_all) dataset. It achieves the following results on the evaluation set: - Loss: 0.3978 - F1 Score: 0.8186 - Accuracy: 0.8186 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.4843 \| 0.54 \| 200 \| 0.4551 \| 0.7931 \| 0.7932 \| \| 0.4414 \| 1.08 \| 400 \| 0.4512 \| 0.7917 \| 0.7924 \| \| 0.4221 \| 1.62 \| 600 \| 0.4357 \| 0.8025 \| 0.8025 \| \| 0.4209 \| 2.16 \| 800 \| 0.4342 \| 0.8067 \| 0.8068 \| \| 0.4146 \| 2.7 \| 1000 \| 0.4316 \| 0.8009 \| 0.8012 \| \| 0.4099 \| 3.24 \| 1200 \| 0.4236 \| 0.8056 \| 0.8056 \| \| 0.407 \| 3.78 \| 1400 \| 0.4227 \| 0.8052 \| 0.8052 \| \| 0.3988 \| 4.32 \| 1600 \| 0.4267 \| 0.8078 \| 0.8078 \| \| 0.4212 \| 4.86 \| 1800 \| 0.6660 \| 0.5306 \| 0.5797 \| \| 0.4294 \| 5.41 \| 2000 \| 0.4280 \| 0.8080 \| 0.8081 \| \| 0.4014 \| 5.95 \| 2200 \| 0.4332 \| 0.8031 \| 0.8032 \| \| 0.3956 \| 6.49 \| 2400 \| 0.4251 \| 0.8082 \| 0.8083 \| \| 0.3886 \| 7.03 \| 2600 \| 0.4256 \| 0.8053 \| 0.8054 \| \| 0.3866 \| 7.57 \| 2800 \| 0.4289 \| 0.8099 \| 0.8100 \| \| 0.3826 \| 8.11 \| 3000 \| 0.4269 \| 0.8113 \| 0.8113 \| \| 0.3803 \| 8.65 \| 3200 \| 0.4216 \| 0.8125 \| 0.8125 \| \| 0.3833 \| 9.19 \| 3400 \| 0.4302 \| 0.8106 \| 0.8106 \| \| 0.3784 \| 9.73 \| 3600 \| 0.4237 \| 0.8113 \| 0.8115 \| \| 0.3788 \| 10.27 \| 3800 \| 0.4240 \| 0.8122 \| 0.8122 \| \| 0.3727 \| 10.81 \| 4000 \| 0.4327 \| 0.8067 \| 0.8068 \| \| 0.3672 \| 11.35 \| 4200 \| 0.4273 \| 0.8111 \| 0.8111 \| \| 0.3704 \| 11.89 \| 4400 \| 0.4169 \| 0.8199 \| 0.8199 \| \| 0.3635 \| 12.43 \| 4600 \| 0.4387 \| 0.8078 \| 0.8079 \| \| 0.3655 \| 12.97 \| 4800 \| 0.4228 \| 0.8125 \| 0.8125 \| \| 0.3584 \| 13.51 \| 5000 \| 0.4291 \| 0.8121 \| 0.8123 \| \| 0.3616 \| 14.05 \| 5200 \| 0.4258 \| 0.8169 \| 0.8169 \| \| 0.3541 \| 14.59 \| 5400 \| 0.4300 \| 0.8165 \| 0.8166 \| \| 0.3565 \| 15.14 \| 5600 \| 0.4357 \| 0.8097 \| 0.8098 \| \| 0.3537 \| 15.68 \| 5800 \| 0.4256 \| 0.8166 \| 0.8167 \| \| 0.349 \| 16.22 \| 6000 \| 0.4285 \| 0.8159 \| 0.8162 \| \| 0.353 \| 16.76 \| 6200 \| 0.4206 \| 0.8162 \| 0.8162 \| \| 0.3506 \| 17.3 \| 6400 \| 0.4265 \| 0.8168 \| 0.8169 \| \| 0.3503 \| 17.84 \| 6600 \| 0.4274 \| 0.8150 \| 0.8150 \| \| 0.34 \| 18.38 \| 6800 \| 0.4334 \| 0.8080 \| 0.8084 \| \| 0.3473 \| 18.92 \| 7000 \| 0.4236 \| 0.8187 \| 0.8187 \| \| 0.3364 \| 19.46 \| 7200 \| 0.4313 \| 0.8170 \| 0.8171 \| \| 0.3468 \| 20.0 \| 7400 \| 0.4267 \| 0.8149 \| 0.8149 \| \| 0.3386 \| 20.54 \| 7600 \| 0.4296 \| 0.8139 \| 0.8140 \| \| 0.3378 \| 21.08 \| 7800 \| 0.4356 \| 0.8194 \| 0.8194 \| \| 0.3377 \| 21.62 \| 8000 \| 0.4284 \| 0.8138 \| 0.8139 \| \| 0.3363 \| 22.16 \| 8200 \| 0.4275 \| 0.8155 \| 0.8155 \| \| 0.338 \| 22.7 \| 8400 \| 0.4294 \| 0.8171 \| 0.8172 \| \| 0.3307 \| 23.24 \| 8600 \| 0.4267 \| 0.8167 \| 0.8167 \| \| 0.3298 \| 23.78 \| 8800 \| 0.4318 \| 0.8170 \| 0.8171 \| \| 0.3301 \| 24.32 \| 9000 \| 0.4314 \| 0.8150 \| 0.8150 \| \| 0.3309 \| 24.86 \| 9200 \| 0.4294 \| 0.8162 \| 0.8162 \| \| 0.3371 \| 25.41 \| 9400 \| 0.4296 \| 0.8155 \| 0.8155 \| \| 0.3246 \| 25.95 \| 9600 \| 0.4311 \| 0.8160 \| 0.8160 \| \| 0.3297 \| 26.49 \| 9800 \| 0.4322 \| 0.8160 \| 0.8160 \| \| 0.3249 \| 27.03 \| 10000 \| 0.4333 \| 0.8160 \| 0.8160 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_prom_prom_core_all-seqsight_4096_512_46M-L32_f", "results": []}]}	mahdibaghbanzadeh/GUE_prom_prom_core_all-seqsight_4096_512_46M-L32_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_46M", "region:us" ]	null	2024-04-26T19:36:17+00:00	[]	[]	TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us	GUE\_prom\_prom\_core\_all-seqsight\_4096\_512\_46M-L32\_f ========================================================== This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_46M on the mahdibaghbanzadeh/GUE\_prom\_prom\_core\_all dataset. It achieves the following results on the evaluation set: * Loss: 0.3978 * F1 Score: 0.8186 * Accuracy: 0.8186 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]	[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]
null	peft	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.2.dev0	{"library_name": "peft", "base_model": "meta-llama/Meta-Llama-3-8B"}	yiyic/llama-text-entprop-lora-clf-epoch-3	null	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Meta-Llama-3-8B", "region:us" ]	null	2024-04-26T19:38:40+00:00	[ "1910.09700" ]	[]	TAGS #peft #safetensors #arxiv-1910.09700 #base_model-meta-llama/Meta-Llama-3-8B #region-us	# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.7.2.dev0	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.7.2.dev0" ]	[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-meta-llama/Meta-Llama-3-8B #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.7.2.dev0" ]
text-to-image	diffusers	<!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # DreamBooth - Yulaaa/dogbooth This is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on a photo of [v]dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]	{"license": "creativeml-openrail-m", "library_name": "diffusers", "tags": ["text-to-image", "dreambooth", "diffusers-training", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "diffusers-training", "stable-diffusion", "stable-diffusion-diffusers"], "base_model": "stabilityai/stable-diffusion-2-1", "inference": true, "instance_prompt": "a photo of [v]dog"}	Yulaaa/dogbooth	null	[ "diffusers", "tensorboard", "safetensors", "text-to-image", "dreambooth", "diffusers-training", "stable-diffusion", "stable-diffusion-diffusers", "base_model:stabilityai/stable-diffusion-2-1", "license:creativeml-openrail-m", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	null	2024-04-26T19:38:49+00:00	[]	[]	TAGS #diffusers #tensorboard #safetensors #text-to-image #dreambooth #diffusers-training #stable-diffusion #stable-diffusion-diffusers #base_model-stabilityai/stable-diffusion-2-1 #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us	# DreamBooth - Yulaaa/dogbooth This is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on a photo of [v]dog using DreamBooth. You can find some example images in the following. DreamBooth for the text encoder was enabled: False. ## Intended uses & limitations #### How to use #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]	[ "# DreamBooth - Yulaaa/dogbooth\n\nThis is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on a photo of [v]dog using DreamBooth.\nYou can find some example images in the following. \n\n\n\nDreamBooth for the text encoder was enabled: False.", "## Intended uses & limitations", "#### How to use", "#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]", "## Training details\n\n[TODO: describe the data used to train the model]" ]	[ "TAGS\n#diffusers #tensorboard #safetensors #text-to-image #dreambooth #diffusers-training #stable-diffusion #stable-diffusion-diffusers #base_model-stabilityai/stable-diffusion-2-1 #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n", "# DreamBooth - Yulaaa/dogbooth\n\nThis is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on a photo of [v]dog using DreamBooth.\nYou can find some example images in the following. \n\n\n\nDreamBooth for the text encoder was enabled: False.", "## Intended uses & limitations", "#### How to use", "#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]", "## Training details\n\n[TODO: describe the data used to train the model]" ]
null	peft	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. 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text-generation	transformers	<div align="center"> <img width="260px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/BrQCb95lmEIFz79QAmoNA.png"></div> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/2FhDh8NDvMl7iSxbQz9BP.png) <div align="center"> <h1>Advancing Open-source Large Language Models in Medical Domain</h1> </div> <p align="center" style="margin-top: 0px;"> <a href="https://colab.research.google.com/drive/1F5oV20InEYeAJGmBwYF9NM_QhLmjBkKJ?usp=sharing"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="OpenChat Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 10px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">Online Demo</span> </a> \| <a href="https://github.com/openlifescience-ai"> <img src="https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png" alt="GitHub Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">GitHub</span> </a> \| <a href="#"> <img src="https://github.com/alpayariyak/openchat/blob/master/assets/arxiv-logomark-small-square-border.png?raw=true" alt="ArXiv Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style="margin-right: 5px;">Paper</span> </a> \| <a href="https://discord.gg/A5Fjf5zC69"> <img src="https://cloud.githubusercontent.com/assets/6291467/26705903/96c2d66e-477c-11e7-9f4e-f3c0efe96c9a.png" alt="Discord Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text">Discord</span> </a> </p> ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/KGmRE5w2sepNtwsEu8t7K.jpeg) Introducing OpenBioLLM-8B: A State-of-the-Art Open Source Biomedical Large Language Model OpenBioLLM-8B is an advanced open source language model designed specifically for the biomedical domain. Developed by Saama AI Labs, this model leverages cutting-edge techniques to achieve state-of-the-art performance on a wide range of biomedical tasks. 🏥 Biomedical Specialization: OpenBioLLM-8B is tailored for the unique language and knowledge requirements of the medical and life sciences fields. It was fine-tuned on a vast corpus of high-quality biomedical data, enabling it to understand and generate text with domain-specific accuracy and fluency. 🎓 Superior Performance: With 8 billion parameters, OpenBioLLM-8B outperforms other open source biomedical language models of similar scale. It has also demonstrated better results compared to larger proprietary & open-source models like GPT-3.5 and Meditron-70B on biomedical benchmarks. 🧠 Advanced Training Techniques: OpenBioLLM-8B builds upon the powerful foundations of the Meta-Llama-3-8B and [Meta-Llama-3-8B](meta-llama/Meta-Llama-3-8B) models. It incorporates the DPO dataset and fine-tuning recipe along with a custom diverse medical instruction dataset. Key components of the training pipeline include: <div align="center"> <img width="1200px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/oPchsJsEpQoGcGXVbh7YS.png"> </div> - Policy Optimization: [Direct Preference Optimization: Your Language Model is Secretly a Reward Model (DPO)](https://arxiv.org/abs/2305.18290) - Ranking Dataset: [berkeley-nest/Nectar](https://huggingface.co/datasets/berkeley-nest/Nectar) - Fine-tuning dataset: Custom Medical Instruct dataset (We plan to release a sample training dataset in our upcoming paper; please stay updated) This combination of cutting-edge techniques enables OpenBioLLM-8B to align with key capabilities and preferences for biomedical applications. ⚙️ Release Details: - Model Size: 8 billion parameters - Quantization: Optimized quantized versions available [Here](https://huggingface.co/aaditya/OpenBioLLM-8B-GGUF) - Language(s) (NLP): en - Developed By: [Ankit Pal (Aaditya Ura)](https://aadityaura.github.io/) from Saama AI Labs - License: Meta-Llama License - Fine-tuned from models: [meta-llama/Meta-Llama-3-8B](meta-llama/Meta-Llama-3-8B) - Resources for more information: - Paper: Coming soon The model can be fine-tuned for more specialized tasks and datasets as needed. OpenBioLLM-8B represents an important step forward in democratizing advanced language AI for the biomedical community. By leveraging state-of-the-art architectures and training techniques from leading open source efforts like Llama-3, we have created a powerful tool to accelerate innovation and discovery in healthcare and the life sciences. We are excited to share OpenBioLLM-8B with researchers and developers around the world. ### Use with transformers Important: Please use the exact chat template provided by Llama-3 instruct version. Otherwise there will be a degradation in the performance. The model output can be verbose in rare cases. Please consider setting temperature = 0 to make this happen less. See the snippet below for usage with Transformers: ```python import transformers import torch model_id = "aaditya/OpenBioLLM-Llama3-8B" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device="auto", ) messages = [ {"role": "system", "content": "You are an expert and experienced from the healthcare and biomedical domain with extensive medical knowledge and practical experience. Your name is OpenBioLLM, and you were developed by Saama AI Labs. who's willing to help answer the user's query with explanation. In your explanation, leverage your deep medical expertise such as relevant anatomical structures, physiological processes, diagnostic criteria, treatment guidelines, or other pertinent medical concepts. Use precise medical terminology while still aiming to make the explanation clear and accessible to a general audience."}, {"role": "user", "content": "How can i split a 3mg or 4mg waefin pill so i can get a 2.5mg pill?"}, ] prompt = pipeline.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) terminators = [ pipeline.tokenizer.eos_token_id, pipeline.tokenizer.convert_tokens_to_ids("<\|eot_id\|>") ] outputs = pipeline( prompt, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.0, top_p=0.9, ) print(outputs[0]["generated_text"][len(prompt):]) ``` ## Training procedure ### Training hyperparameters <details> <summary>Click to see details</summary> - learning_rate: 0.0002 - lr_scheduler: cosine - train_batch_size: 12 - eval_batch_size: 8 - GPU: H100 80GB SXM5 - num_devices: 1 - optimizer: adamw_bnb_8bit - lr_scheduler_warmup_steps: 100 - num_epochs: 4 </details> ### Peft hyperparameters <details> <summary>Click to see details</summary> - adapter: qlora - lora_r: 128 - lora_alpha: 256 - lora_dropout: 0.05 - lora_target_linear: true -lora_target_modules: - q_proj - v_proj - k_proj - o_proj - gate_proj - down_proj - up_proj </details> ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.1 - Axolotl - Lm harness for evaluation # Benchmark Results 🔥 OpenBioLLM-8B demonstrates superior performance compared to larger models, such as GPT-3.5, Meditron-70B across 9 diverse biomedical datasets, achieving state-of-the-art results with an average score of 72.50%, despite having a significantly smaller parameter count. The model's strong performance in domain-specific tasks, such as Clinical KG, Medical Genetics, and PubMedQA, highlights its ability to effectively capture and apply biomedical knowledge. 🚨 The GPT-4, Med-PaLM-1, and Med-PaLM-2 results are taken from their official papers. Since Med-PaLM doesn't provide zero-shot accuracy, we are using 5-shot accuracy from their paper for comparison. All results presented are in the zero-shot setting, except for Med-PaLM-2 and Med-PaLM-1, which use 5-shot accuracy. \| \| Clinical KG \| Medical Genetics \| Anatomy \| Pro Medicine \| College Biology \| College Medicine \| MedQA 4 opts \| PubMedQA \| MedMCQA \| Avg \| \|--------------------\|-------------\|------------------\|---------\|--------------\|-----------------\|------------------\|--------------\|----------\|---------\|-------\| \| OpenBioLLM-70B \| 92.93 \| 93.197 \| 83.904 \| 93.75 \| 93.827 \| 85.749 \| 78.162 \| 78.97 \| 74.014 \| 86.05588 \| \| Med-PaLM-2 (5-shot) \| 88.3 \| 90 \| 77.8 \| 95.2 \| 94.4 \| 80.9 \| 79.7 \| 79.2 \| 71.3 \| 84.08 \| \| GPT-4 \| 86.04 \| 91 \| 80 \| 93.01 \| 95.14 \| 76.88 \| 78.87 \| 75.2 \| 69.52 \| 82.85 \| \| Med-PaLM-1 (Flan-PaLM, 5-shot) \| 80.4 \| 75 \| 63.7 \| 83.8 \| 88.9 \| 76.3 \| 67.6 \| 79 \| 57.6 \| 74.7 \| \| OpenBioLLM-8B \| 76.101 \| 86.1 \| 69.829 \| 78.21 \| 84.213 \| 68.042 \| 58.993 \| 74.12 \| 56.913 \| 72.502 \| \| Gemini-1.0 \| 76.7 \| 75.8 \| 66.7 \| 77.7 \| 88 \| 69.2 \| 58 \| 70.7 \| 54.3 \| 70.79 \| \| GPT-3.5 Turbo 1106 \| 74.71 \| 74 \| 72.79 \| 72.79 \| 72.91 \| 64.73 \| 57.71 \| 72.66 \| 53.79 \| 66 \| \| Meditron-70B \| 66.79 \| 69 \| 53.33 \| 71.69 \| 76.38 \| 63 \| 57.1 \| 76.6 \| 46.85 \| 64.52 \| \| gemma-7b \| 69.81 \| 70 \| 59.26 \| 66.18 \| 79.86 \| 60.12 \| 47.21 \| 76.2 \| 48.96 \| 64.18 \| \| Mistral-7B-v0.1 \| 68.68 \| 71 \| 55.56 \| 68.38 \| 68.06 \| 59.54 \| 50.82 \| 75.4 \| 48.2 \| 62.85 \| \| Apollo-7B \| 62.26 \| 72 \| 61.48 \| 69.12 \| 70.83 \| 55.49 \| 55.22 \| 39.8 \| 53.77 \| 60 \| \| MedAlpaca-7b \| 57.36 \| 69 \| 57.04 \| 67.28 \| 65.28 \| 54.34 \| 41.71 \| 72.8 \| 37.51 \| 58.03 \| \| BioMistral-7B \| 59.9 \| 64 \| 56.5 \| 60.4 \| 59 \| 54.7 \| 50.6 \| 77.5 \| 48.1 \| 57.3 \| \| AlpaCare-llama2-7b \| 49.81 \| 49 \| 45.92 \| 33.82 \| 50 \| 43.35 \| 29.77 \| 72.2 \| 34.42 \| 45.36 \| \| ClinicalGPT \| 30.56 \| 27 \| 30.37 \| 19.48 \| 25 \| 24.27 \| 26.08 \| 63.8 \| 28.18 \| 30.52 \| <div align="center"> <img width="1600px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/_SzdcJSBjZyo8RS1bTEkP.png"> </div> ## Detailed Medical Subjectwise accuracy ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/UXF-V0col0Z0sS6BGPBkE.png) # Use Cases & Examples 🚨 Below results are from the quantized version of OpenBioLLM-70B # Summarize Clinical Notes OpenBioLLM-70B can efficiently analyze and summarize complex clinical notes, EHR data, and discharge summaries, extracting key information and generating concise, structured summaries ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/xdwdBgOxNi_TfML0hKlI8.png) # Answer Medical Questions OpenBioLLM-70B can provide answers to a wide range of medical questions. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/zO95GlwOQEZqCKQF69mE6.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/OKBczKw7gWeW5xsuDpc27.png) <details> <summary>Click to see details</summary> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/eJGHT5khppYvJb8fQ-YW4.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/Cnbwrqa_-ORHRuNRC2P6Y.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/J9DhdcvukAc9mnnW9fj2C.png) </details> # Clinical Entity Recognition OpenBioLLM-70B can perform advanced clinical entity recognition by identifying and extracting key medical concepts, such as diseases, symptoms, medications, procedures, and anatomical structures, from unstructured clinical text. By leveraging its deep understanding of medical terminology and context, the model can accurately annotate and categorize clinical entities, enabling more efficient information retrieval, data analysis, and knowledge discovery from electronic health records, research articles, and other biomedical text sources. This capability can support various downstream applications, such as clinical decision support, pharmacovigilance, and medical research. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/_69BW4k9LVABFwtxixL45.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/DKy5wYCoPhoPPUc1-x8_J.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/7WD9zCCBZT4-4XlfnIQjl.png) # Biomarkers Extraction ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/ZttoM4AiteT7gFYVhjIpN.png) # Classification OpenBioLLM-70B can perform various biomedical classification tasks, such as disease prediction, sentiment analysis, medical document categorization ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/Bf5MW1d75qT-1F_TR_hC0.png) # De-Identification OpenBioLLM-70B can detect and remove personally identifiable information (PII) from medical records, ensuring patient privacy and compliance with data protection regulations like HIPAA. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/hKX4kzm--Tw5bj6K78msy.png) Advisory Notice! While OpenBioLLM-70B & 8B leverages high-quality data sources, its outputs may still contain inaccuracies, biases, or misalignments that could pose risks if relied upon for medical decision-making without further testing and refinement. The model's performance has not yet been rigorously evaluated in randomized controlled trials or real-world healthcare environments. Therefore, we strongly advise against using OpenBioLLM-70B & 8B for any direct patient care, clinical decision support, or other professional medical purposes at this time. Its use should be limited to research, development, and exploratory applications by qualified individuals who understand its limitations. OpenBioLLM-70B & 8B are intended solely as a research tool to assist healthcare professionals and should never be considered a replacement for the professional judgment and expertise of a qualified medical doctor. Appropriately adapting and validating OpenBioLLM-70B & 8B for specific medical use cases would require significant additional work, potentially including: - Thorough testing and evaluation in relevant clinical scenarios - Alignment with evidence-based guidelines and best practices - Mitigation of potential biases and failure modes - Integration with human oversight and interpretation - Compliance with regulatory and ethical standards Always consult a qualified healthcare provider for personal medical needs. # Citation If you find OpenBioLLM-70B & 8B useful in your work, please cite the model as follows: ``` @misc{OpenBioLLMs, author = {Ankit Pal, Malaikannan Sankarasubbu}, title = {OpenBioLLMs: Advancing Open-Source Large Language Models for Healthcare and Life Sciences}, year = {2024}, publisher = {Hugging Face}, journal = {Hugging Face repository}, howpublished = {\url{https://huggingface.co/aaditya/OpenBioLLM-Llama3-70B}} } ``` The accompanying paper is currently in progress and will be released soon. <div align="center"> <h2> 💌 Contact </h2> </div> We look forward to hearing you and collaborating on this exciting project! Contributors: - [Ankit Pal (Aaditya Ura)](https://aadityaura.github.io/) [aadityaura at gmail dot com] - Saama AI Labs - Note: I am looking for a funded PhD opportunity, especially if it fits my Responsible Generative AI, Multimodal LLMs, Geometric Deep Learning, and Healthcare AI skillset. # References We thank the [Meta Team](meta-llama/Meta-Llama-3-70B-Instruct) for their amazing models! Result sources - [1] GPT-4 [Capabilities of GPT-4 on Medical Challenge Problems] (https://arxiv.org/abs/2303.13375) - [2] Med-PaLM-1 [Large Language Models Encode Clinical Knowledge](https://arxiv.org/abs/2212.13138) - [3] Med-PaLM-2 [Towards Expert-Level Medical Question Answering with Large Language Models](https://arxiv.org/abs/2305.09617) - [4] Gemini-1.0 [Gemini Goes to Med School](https://arxiv.org/abs/2402.07023)	{"language": ["en"], "license": "llama3", "tags": ["llama-3", "llama", "Mixtral", "instruct", "finetune", "chatml", "DPO", "RLHF", "gpt4", "distillation"], "base_model": "meta-llama/Meta-Llama-3-8B", "widget": [{"example_title": "OpenBioLLM-8B", "messages": [{"role": "system", "content": "You are an expert and experienced from the healthcare and biomedical domain with extensive medical knowledge and practical experience."}, {"role": "user", "content": "How long does it take for newborn jaundice to go away?"}], "output": {"text": "Newborn jaundice, also known as neonatal jaundice, is a common condition in newborns where the yellowing of the skin and eyes occurs due to an elevated level of bilirubin in the blood. Bilirubin is a yellow pigment that forms when red blood cells break down. In most cases, newborn jaundice resolves on its own without any specific treatment.\nThe duration of newborn jaundice can vary depending on several factors such as the underlying cause, gestational age at birth, and individual variations in bilirubin metabolism. Here are some general guidelines:\n1. Physiological jaundice: This is the most common type of newborn jaundice and usually appears within 24-72 hours after birth. It tends to peak between the second and fifth day of life and gradually improves over the next week or two. By the time the baby is one week old, the jaundice should have mostly resolved. 2. Breast milk jaundice: This type of jaundice occurs in breastfed babies and may appear later than physiological jaundice, typically between the fifth and fourteenth day of life. It tends to persist for a longer duration but usually resolves within six weeks after birth. 3. Pathological jaundice: This type of jaundice is less common and occurs due to an underlying medical condition that affects bilirubin metabolism or liver function. The duration of pathological jaundice depends on the specific cause and may require treatment.\nIt's important for parents to monitor their newborn's jaundice closely and seek medical advice if the jaundice progresses rapidly, becomes severe, or is accompanied by other symptoms such as poor feeding, lethargy, or excessive sleepiness. In these cases, further evaluation and management may be necessary. Remember that each baby is unique, and the timing of jaundice resolution can vary. If you have concerns about your newborn's jaundice, it's always best to consult with a healthcare professional for personalized advice and guidance."}}], "model-index": [{"name": "OpenBioLLM-8B", "results": []}]}	LoneStriker/OpenBioLLM-Llama3-8B-3.0bpw-h6-exl2	null	[ "transformers", "pytorch", "llama", "text-generation", "llama-3", "Mixtral", "instruct", "finetune", "chatml", "DPO", "RLHF", "gpt4", "distillation", "en", "arxiv:2305.18290", "arxiv:2303.13375", "arxiv:2212.13138", "arxiv:2305.09617", "arxiv:2402.07023", "base_model:meta-llama/Meta-Llama-3-8B", "license:llama3", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "3-bit", "region:us" ]	null	2024-04-26T19:39:48+00:00	[ "2305.18290", "2303.13375", "2212.13138", "2305.09617", "2402.07023" ]	[ "en" ]	TAGS #transformers #pytorch #llama #text-generation #llama-3 #Mixtral #instruct #finetune #chatml #DPO #RLHF #gpt4 #distillation #en #arxiv-2305.18290 #arxiv-2303.13375 #arxiv-2212.13138 #arxiv-2305.09617 #arxiv-2402.07023 #base_model-meta-llama/Meta-Llama-3-8B #license-llama3 #autotrain_compatible #endpoints_compatible #text-generation-inference #3-bit #region-us	![](URL <p>!image/png</p> <div align=) Advancing Open-source Large Language Models in Medical Domain ============================================================= Online Demo \| GitHub \| [![](URL alt=)](#) \| Discord !image/jpeg Introducing OpenBioLLM-8B: A State-of-the-Art Open Source Biomedical Large Language Model OpenBioLLM-8B is an advanced open source language model designed specifically for the biomedical domain. Developed by Saama AI Labs, this model leverages cutting-edge techniques to achieve state-of-the-art performance on a wide range of biomedical tasks. Biomedical Specialization: OpenBioLLM-8B is tailored for the unique language and knowledge requirements of the medical and life sciences fields. It was fine-tuned on a vast corpus of high-quality biomedical data, enabling it to understand and generate text with domain-specific accuracy and fluency. Superior Performance: With 8 billion parameters, OpenBioLLM-8B outperforms other open source biomedical language models of similar scale. It has also demonstrated better results compared to larger proprietary & open-source models like GPT-3.5 and Meditron-70B on biomedical benchmarks. Advanced Training Techniques: OpenBioLLM-8B builds upon the powerful foundations of the Meta-Llama-3-8B and Meta-Llama-3-8B models. It incorporates the DPO dataset and fine-tuning recipe along with a custom diverse medical instruction dataset. Key components of the training pipeline include: ![](URL </div> <ul> <li>Policy Optimization: Direct Preference Optimization: Your Language Model is Secretly a Reward Model (DPO)</li> <li>Ranking Dataset: berkeley-nest/Nectar</li> <li>Fine-tuning dataset: Custom Medical Instruct dataset (We plan to release a sample training dataset in our upcoming paper; please stay updated)</li> </ul> <p>This combination of cutting-edge techniques enables OpenBioLLM-8B to align with key capabilities and preferences for biomedical applications.</p> <p>️ Release Details:</p> <ul> <li>Model Size: 8 billion parameters</li> <li>Quantization: Optimized quantized versions available Here</li> <li>Language(s) (NLP): en</li> <li>Developed By: Ankit Pal (Aaditya Ura) from Saama AI Labs</li> <li>License: Meta-Llama License</li> <li>Fine-tuned from models: meta-llama/Meta-Llama-3-8B</li> <li>Resources for more information: <ul> <li>Paper: Coming soon</li> </ul> </li> </ul> <p>The model can be fine-tuned for more specialized tasks and datasets as needed.</p> <p>OpenBioLLM-8B represents an important step forward in democratizing advanced language AI for the biomedical community. By leveraging state-of-the-art architectures and training techniques from leading open source efforts like Llama-3, we have created a powerful tool to accelerate innovation and discovery in healthcare and the life sciences.</p> <p>We are excited to share OpenBioLLM-8B with researchers and developers around the world.</p> <h3>Use with transformers</h3> <p>Important: Please use the exact chat template provided by Llama-3 instruct version. Otherwise there will be a degradation in the performance. The model output can be verbose in rare cases. Please consider setting temperature = 0 to make this happen less.</p> <p>See the snippet below for usage with Transformers:</p> <h2>Training procedure</h2> <h3>Training hyperparameters</h3> <details> <summary>Click to see details</summary> <ul> <li>learning_rate: 0.0002</li> <li>lr_scheduler: cosine</li> <li>train_batch_size: 12</li> <li>eval_batch_size: 8</li> <li>GPU: H100 80GB SXM5</li> <li>num_devices: 1</li> <li>optimizer: adamw_bnb_8bit</li> <li>lr_scheduler_warmup_steps: 100</li> <li>num_epochs: 4</li> </ul> </details> <h3>Peft hyperparameters</h3> <details> <summary>Click to see details</summary> <ul> <li>adapter: qlora</li> <li>lora_r: 128</li> <li>lora_alpha: 256</li> <li>lora_dropout: 0.05</li> <li>lora_target_linear: true</li> </ul> <p>-lora_target_modules:</p> <ul> <li>q_proj</li> <li>v_proj</li> <li>k_proj</li> <li>o_proj</li> <li>gate_proj</li> <li>down_proj</li> <li>up_proj</li> </ul> </details> <h3>Training results</h3> <h3>Framework versions</h3> <ul> <li>Transformers 4.39.3</li> <li>Pytorch 2.1.2+cu121</li> <li>Datasets 2.18.0</li> <li>Tokenizers 0.15.1</li> <li>Axolotl</li> <li>Lm harness for evaluation</li> </ul> <h1>Benchmark Results</h1> <p>OpenBioLLM-8B demonstrates superior performance compared to larger models, such as GPT-3.5, Meditron-70B across 9 diverse biomedical datasets, achieving state-of-the-art results with an average score of 72.50%, despite having a significantly smaller parameter count. The model's strong performance in domain-specific tasks, such as Clinical KG, Medical Genetics, and PubMedQA, highlights its ability to effectively capture and apply biomedical knowledge.</p> <p>The GPT-4, Med-PaLM-1, and Med-PaLM-2 results are taken from their official papers. Since Med-PaLM doesn't provide zero-shot accuracy, we are using 5-shot accuracy from their paper for comparison. All results presented are in the zero-shot setting, except for Med-PaLM-2 and Med-PaLM-1, which use 5-shot accuracy.</p> <p></p> <div align=) ![](URL </div> <h2>Detailed Medical Subjectwise accuracy</h2> <p>!image/png</p> <h1>Use Cases & Examples</h1> <p>Below results are from the quantized version of OpenBioLLM-70B</p> <h1>Summarize Clinical Notes</h1> <p>OpenBioLLM-70B can efficiently analyze and summarize complex clinical notes, EHR data, and discharge summaries, extracting key information and generating concise, structured summaries</p> <p>!image/png</p> <h1>Answer Medical Questions</h1> <p>OpenBioLLM-70B can provide answers to a wide range of medical questions.</p> <p>!image/png !image/png</p> <details> <summary>Click to see details</summary> <p>!image/png !image/png !image/png</p> </details> <h1>Clinical Entity Recognition</h1> <p>OpenBioLLM-70B can perform advanced clinical entity recognition by identifying and extracting key medical concepts, such as diseases, symptoms, medications, procedures, and anatomical structures, from unstructured clinical text. By leveraging its deep understanding of medical terminology and context, the model can accurately annotate and categorize clinical entities, enabling more efficient information retrieval, data analysis, and knowledge discovery from electronic health records, research articles, and other biomedical text sources. This capability can support various downstream applications, such as clinical decision support, pharmacovigilance, and medical research.</p> <p>!image/png !image/png !image/png</p> <h1>Biomarkers Extraction</h1> <p>!image/png</p> <h1>Classification</h1> <p>OpenBioLLM-70B can perform various biomedical classification tasks, such as disease prediction, sentiment analysis, medical document categorization</p> <p>!image/png</p> <h1>De-Identification</h1> <p>OpenBioLLM-70B can detect and remove personally identifiable information (PII) from medical records, ensuring patient privacy and compliance with data protection regulations like HIPAA.</p> <p>!image/png</p> <p>Advisory Notice!</p> <p>While OpenBioLLM-70B & 8B leverages high-quality data sources, its outputs may still contain inaccuracies, biases, or misalignments that could pose risks if relied upon for medical decision-making without further testing and refinement. The model's performance has not yet been rigorously evaluated in randomized controlled trials or real-world healthcare environments.</p> <p>Therefore, we strongly advise against using OpenBioLLM-70B & 8B for any direct patient care, clinical decision support, or other professional medical purposes at this time. Its use should be limited to research, development, and exploratory applications by qualified individuals who understand its limitations. OpenBioLLM-70B & 8B are intended solely as a research tool to assist healthcare professionals and should never be considered a replacement for the professional judgment and expertise of a qualified medical doctor.</p> <p>Appropriately adapting and validating OpenBioLLM-70B & 8B for specific medical use cases would require significant additional work, potentially including:</p> <ul> <li>Thorough testing and evaluation in relevant clinical scenarios</li> <li>Alignment with evidence-based guidelines and best practices</li> <li>Mitigation of potential biases and failure modes</li> <li>Integration with human oversight and interpretation</li> <li>Compliance with regulatory and ethical standards</li> </ul> <p>Always consult a qualified healthcare provider for personal medical needs.</p> <p>If you find OpenBioLLM-70B & 8B useful in your work, please cite the model as follows:</p> <p>The accompanying paper is currently in progress and will be released soon.</p> <div align=) Contact -------- We look forward to hearing you and collaborating on this exciting project! Contributors: * Ankit Pal (Aaditya Ura) [aadityaura at gmail dot com] * Saama AI Labs * Note: I am looking for a funded PhD opportunity, especially if it fits my Responsible Generative AI, Multimodal LLMs, Geometric Deep Learning, and Healthcare AI skillset. References ========== We thank the Meta Team for their amazing models! Result sources * [1] GPT-4 [Capabilities of GPT-4 on Medical Challenge Problems] (URL * [2] Med-PaLM-1 Large Language Models Encode Clinical Knowledge * [3] Med-PaLM-2 Towards Expert-Level Medical Question Answering with Large Language Models * [4] Gemini-1.0 Gemini Goes to Med School	[]	[ "TAGS\n#transformers #pytorch #llama #text-generation #llama-3 #Mixtral #instruct #finetune #chatml #DPO #RLHF #gpt4 #distillation #en #arxiv-2305.18290 #arxiv-2303.13375 #arxiv-2212.13138 #arxiv-2305.09617 #arxiv-2402.07023 #base_model-meta-llama/Meta-Llama-3-8B #license-llama3 #autotrain_compatible #endpoints_compatible #text-generation-inference #3-bit #region-us \n" ]
reinforcement-learning	stable-baselines3	# PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```	{"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "266.91 +/- 17.69", "name": "mean_reward", "verified": false}]}]}]}	pkroeger/ppo-LunarLander-v2	null	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	null	2024-04-26T19:40:15+00:00	[]	[]	TAGS #stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us	# PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library. ## Usage (with Stable-baselines3) TODO: Add your code	[ "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]	[ "TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]
null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # GUE_prom_prom_core_notata-seqsight_4096_512_46M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_notata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_notata) dataset. It achieves the following results on the evaluation set: - Loss: 0.3766 - F1 Score: 0.8417 - Accuracy: 0.8417 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.5055 \| 0.6 \| 200 \| 0.4114 \| 0.8087 \| 0.8087 \| \| 0.4339 \| 1.2 \| 400 \| 0.4005 \| 0.8166 \| 0.8168 \| \| 0.4152 \| 1.81 \| 600 \| 0.3858 \| 0.8241 \| 0.8242 \| \| 0.4029 \| 2.41 \| 800 \| 0.3732 \| 0.8306 \| 0.8306 \| \| 0.3912 \| 3.01 \| 1000 \| 0.3714 \| 0.8340 \| 0.8340 \| \| 0.3862 \| 3.61 \| 1200 \| 0.3654 \| 0.8355 \| 0.8355 \| \| 0.3884 \| 4.22 \| 1400 \| 0.3670 \| 0.8338 \| 0.8338 \| \| 0.3884 \| 4.82 \| 1600 \| 0.3644 \| 0.8387 \| 0.8387 \| \| 0.3772 \| 5.42 \| 1800 \| 0.3822 \| 0.8308 \| 0.8312 \| \| 0.3821 \| 6.02 \| 2000 \| 0.3655 \| 0.8376 \| 0.8376 \| \| 0.3786 \| 6.63 \| 2200 \| 0.3621 \| 0.8378 \| 0.8378 \| \| 0.3748 \| 7.23 \| 2400 \| 0.3634 \| 0.8398 \| 0.8398 \| \| 0.3774 \| 7.83 \| 2600 \| 0.3631 \| 0.8361 \| 0.8361 \| \| 0.3722 \| 8.43 \| 2800 \| 0.3653 \| 0.8390 \| 0.8393 \| \| 0.376 \| 9.04 \| 3000 \| 0.3608 \| 0.8398 \| 0.8398 \| \| 0.3695 \| 9.64 \| 3200 \| 0.3618 \| 0.8385 \| 0.8385 \| \| 0.3691 \| 10.24 \| 3400 \| 0.3610 \| 0.8391 \| 0.8391 \| \| 0.3683 \| 10.84 \| 3600 \| 0.3593 \| 0.8398 \| 0.8398 \| \| 0.3682 \| 11.45 \| 3800 \| 0.3590 \| 0.8400 \| 0.8400 \| \| 0.3664 \| 12.05 \| 4000 \| 0.3584 \| 0.8414 \| 0.8413 \| \| 0.3641 \| 12.65 \| 4200 \| 0.3592 \| 0.8409 \| 0.8410 \| \| 0.366 \| 13.25 \| 4400 \| 0.3600 \| 0.8414 \| 0.8413 \| \| 0.3684 \| 13.86 \| 4600 \| 0.3574 \| 0.8430 \| 0.8430 \| \| 0.3653 \| 14.46 \| 4800 \| 0.3567 \| 0.8425 \| 0.8425 \| \| 0.3625 \| 15.06 \| 5000 \| 0.3586 \| 0.8438 \| 0.8438 \| \| 0.3605 \| 15.66 \| 5200 \| 0.3565 \| 0.8427 \| 0.8427 \| \| 0.3619 \| 16.27 \| 5400 \| 0.3599 \| 0.8406 \| 0.8406 \| \| 0.3616 \| 16.87 \| 5600 \| 0.3621 \| 0.8414 \| 0.8417 \| \| 0.358 \| 17.47 \| 5800 \| 0.3605 \| 0.8425 \| 0.8427 \| \| 0.3667 \| 18.07 \| 6000 \| 0.3584 \| 0.8435 \| 0.8436 \| \| 0.3582 \| 18.67 \| 6200 \| 0.3545 \| 0.8427 \| 0.8427 \| \| 0.3593 \| 19.28 \| 6400 \| 0.3566 \| 0.8419 \| 0.8419 \| \| 0.3597 \| 19.88 \| 6600 \| 0.3560 \| 0.8455 \| 0.8455 \| \| 0.3538 \| 20.48 \| 6800 \| 0.3586 \| 0.8424 \| 0.8425 \| \| 0.3623 \| 21.08 \| 7000 \| 0.3559 \| 0.8456 \| 0.8457 \| \| 0.3579 \| 21.69 \| 7200 \| 0.3573 \| 0.8434 \| 0.8434 \| \| 0.3526 \| 22.29 \| 7400 \| 0.3570 \| 0.8458 \| 0.8459 \| \| 0.3552 \| 22.89 \| 7600 \| 0.3565 \| 0.8436 \| 0.8436 \| \| 0.3546 \| 23.49 \| 7800 \| 0.3573 \| 0.8441 \| 0.8442 \| \| 0.3606 \| 24.1 \| 8000 \| 0.3579 \| 0.8458 \| 0.8459 \| \| 0.3569 \| 24.7 \| 8200 \| 0.3573 \| 0.8454 \| 0.8455 \| \| 0.3503 \| 25.3 \| 8400 \| 0.3594 \| 0.8450 \| 0.8451 \| \| 0.3577 \| 25.9 \| 8600 \| 0.3570 \| 0.8443 \| 0.8444 \| \| 0.3572 \| 26.51 \| 8800 \| 0.3572 \| 0.8442 \| 0.8442 \| \| 0.3552 \| 27.11 \| 9000 \| 0.3564 \| 0.8434 \| 0.8434 \| \| 0.3591 \| 27.71 \| 9200 \| 0.3562 \| 0.8449 \| 0.8449 \| \| 0.3454 \| 28.31 \| 9400 \| 0.3577 \| 0.8458 \| 0.8459 \| \| 0.3543 \| 28.92 \| 9600 \| 0.3571 \| 0.8468 \| 0.8468 \| \| 0.358 \| 29.52 \| 9800 \| 0.3570 \| 0.8447 \| 0.8447 \| \| 0.3503 \| 30.12 \| 10000 \| 0.3570 \| 0.8462 \| 0.8462 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_prom_prom_core_notata-seqsight_4096_512_46M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_prom_prom_core_notata-seqsight_4096_512_46M-L1_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_46M", "region:us" ]	null	2024-04-26T19:40:16+00:00	[]	[]	TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us	GUE\_prom\_prom\_core\_notata-seqsight\_4096\_512\_46M-L1\_f ============================================================ This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_46M on the mahdibaghbanzadeh/GUE\_prom\_prom\_core\_notata dataset. It achieves the following results on the evaluation set: * Loss: 0.3766 * F1 Score: 0.8417 * Accuracy: 0.8417 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]	[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]
text-generation	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # llama2-lima This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the GAIR/lima dataset. It achieves the following results on the evaluation set: - Loss: 2.5297 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 16 - total_train_batch_size: 128 - total_eval_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.9786 \| 1.0 \| 6 \| 2.6180 \| \| 1.75 \| 2.0 \| 12 \| 1.9988 \| \| 1.5489 \| 3.0 \| 18 \| 1.9909 \| \| 1.3631 \| 4.0 \| 24 \| 1.9766 \| \| 1.0134 \| 5.0 \| 30 \| 2.1003 \| \| 0.8425 \| 6.0 \| 36 \| 2.1683 \| \| 0.7051 \| 7.0 \| 42 \| 2.3737 \| \| 0.594 \| 8.0 \| 48 \| 2.5296 \| \| 0.5107 \| 9.0 \| 54 \| 2.5203 \| \| 0.4594 \| 10.0 \| 60 \| 2.5297 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.2 - Datasets 2.14.6 - Tokenizers 0.15.2	{"license": "llama2", "tags": ["alignment-handbook", "trl", "sft", "generated_from_trainer", "trl", "sft", "generated_from_trainer"], "datasets": ["GAIR/lima"], "base_model": "meta-llama/Llama-2-7b-hf", "model-index": [{"name": "llama2-lima", "results": []}]}	pkarypis/llama2-lima	null	[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "alignment-handbook", "trl", "sft", "generated_from_trainer", "conversational", "dataset:GAIR/lima", "base_model:meta-llama/Llama-2-7b-hf", "license:llama2", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-26T19:41:10+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #llama #text-generation #alignment-handbook #trl #sft #generated_from_trainer #conversational #dataset-GAIR/lima #base_model-meta-llama/Llama-2-7b-hf #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	llama2-lima =========== This model is a fine-tuned version of meta-llama/Llama-2-7b-hf on the GAIR/lima dataset. It achieves the following results on the evaluation set: * Loss: 2.5297 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * distributed\_type: multi-GPU * num\_devices: 16 * total\_train\_batch\_size: 128 * total\_eval\_batch\_size: 128 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: cosine * lr\_scheduler\_warmup\_ratio: 0.1 * num\_epochs: 10.0 ### Training results ### Framework versions * Transformers 4.38.2 * Pytorch 2.1.2 * Datasets 2.14.6 * Tokenizers 0.15.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 16\n* total\\_train\\_batch\\_size: 128\n* total\\_eval\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.1.2\n* Datasets 2.14.6\n* Tokenizers 0.15.2" ]	[ "TAGS\n#transformers #tensorboard #safetensors #llama #text-generation #alignment-handbook #trl #sft #generated_from_trainer #conversational #dataset-GAIR/lima #base_model-meta-llama/Llama-2-7b-hf #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 16\n* total\\_train\\_batch\\_size: 128\n* total\\_eval\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.1.2\n* Datasets 2.14.6\n* Tokenizers 0.15.2" ]
text-generation	transformers	<div align="center"> <img width="260px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/BrQCb95lmEIFz79QAmoNA.png"></div> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/2FhDh8NDvMl7iSxbQz9BP.png) <div align="center"> <h1>Advancing Open-source Large Language Models in Medical Domain</h1> </div> <p align="center" style="margin-top: 0px;"> <a href="https://colab.research.google.com/drive/1F5oV20InEYeAJGmBwYF9NM_QhLmjBkKJ?usp=sharing"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="OpenChat Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 10px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">Online Demo</span> </a> \| <a href="https://github.com/openlifescience-ai"> <img src="https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png" alt="GitHub Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">GitHub</span> </a> \| <a href="#"> <img src="https://github.com/alpayariyak/openchat/blob/master/assets/arxiv-logomark-small-square-border.png?raw=true" alt="ArXiv Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style="margin-right: 5px;">Paper</span> </a> \| <a href="https://discord.gg/A5Fjf5zC69"> <img src="https://cloud.githubusercontent.com/assets/6291467/26705903/96c2d66e-477c-11e7-9f4e-f3c0efe96c9a.png" alt="Discord Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text">Discord</span> </a> </p> ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/KGmRE5w2sepNtwsEu8t7K.jpeg) Introducing OpenBioLLM-8B: A State-of-the-Art Open Source Biomedical Large Language Model OpenBioLLM-8B is an advanced open source language model designed specifically for the biomedical domain. Developed by Saama AI Labs, this model leverages cutting-edge techniques to achieve state-of-the-art performance on a wide range of biomedical tasks. 🏥 Biomedical Specialization: OpenBioLLM-8B is tailored for the unique language and knowledge requirements of the medical and life sciences fields. It was fine-tuned on a vast corpus of high-quality biomedical data, enabling it to understand and generate text with domain-specific accuracy and fluency. 🎓 Superior Performance: With 8 billion parameters, OpenBioLLM-8B outperforms other open source biomedical language models of similar scale. It has also demonstrated better results compared to larger proprietary & open-source models like GPT-3.5 and Meditron-70B on biomedical benchmarks. 🧠 Advanced Training Techniques: OpenBioLLM-8B builds upon the powerful foundations of the Meta-Llama-3-8B and [Meta-Llama-3-8B](meta-llama/Meta-Llama-3-8B) models. It incorporates the DPO dataset and fine-tuning recipe along with a custom diverse medical instruction dataset. Key components of the training pipeline include: <div align="center"> <img width="1200px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/oPchsJsEpQoGcGXVbh7YS.png"> </div> - Policy Optimization: [Direct Preference Optimization: Your Language Model is Secretly a Reward Model (DPO)](https://arxiv.org/abs/2305.18290) - Ranking Dataset: [berkeley-nest/Nectar](https://huggingface.co/datasets/berkeley-nest/Nectar) - Fine-tuning dataset: Custom Medical Instruct dataset (We plan to release a sample training dataset in our upcoming paper; please stay updated) This combination of cutting-edge techniques enables OpenBioLLM-8B to align with key capabilities and preferences for biomedical applications. ⚙️ Release Details: - Model Size: 8 billion parameters - Quantization: Optimized quantized versions available [Here](https://huggingface.co/aaditya/OpenBioLLM-8B-GGUF) - Language(s) (NLP): en - Developed By: [Ankit Pal (Aaditya Ura)](https://aadityaura.github.io/) from Saama AI Labs - License: Meta-Llama License - Fine-tuned from models: [meta-llama/Meta-Llama-3-8B](meta-llama/Meta-Llama-3-8B) - Resources for more information: - Paper: Coming soon The model can be fine-tuned for more specialized tasks and datasets as needed. OpenBioLLM-8B represents an important step forward in democratizing advanced language AI for the biomedical community. By leveraging state-of-the-art architectures and training techniques from leading open source efforts like Llama-3, we have created a powerful tool to accelerate innovation and discovery in healthcare and the life sciences. We are excited to share OpenBioLLM-8B with researchers and developers around the world. ### Use with transformers Important: Please use the exact chat template provided by Llama-3 instruct version. Otherwise there will be a degradation in the performance. The model output can be verbose in rare cases. Please consider setting temperature = 0 to make this happen less. See the snippet below for usage with Transformers: ```python import transformers import torch model_id = "aaditya/OpenBioLLM-Llama3-8B" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device="auto", ) messages = [ {"role": "system", "content": "You are an expert and experienced from the healthcare and biomedical domain with extensive medical knowledge and practical experience. Your name is OpenBioLLM, and you were developed by Saama AI Labs. who's willing to help answer the user's query with explanation. In your explanation, leverage your deep medical expertise such as relevant anatomical structures, physiological processes, diagnostic criteria, treatment guidelines, or other pertinent medical concepts. Use precise medical terminology while still aiming to make the explanation clear and accessible to a general audience."}, {"role": "user", "content": "How can i split a 3mg or 4mg waefin pill so i can get a 2.5mg pill?"}, ] prompt = pipeline.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) terminators = [ pipeline.tokenizer.eos_token_id, pipeline.tokenizer.convert_tokens_to_ids("<\|eot_id\|>") ] outputs = pipeline( prompt, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.0, top_p=0.9, ) print(outputs[0]["generated_text"][len(prompt):]) ``` ## Training procedure ### Training hyperparameters <details> <summary>Click to see details</summary> - learning_rate: 0.0002 - lr_scheduler: cosine - train_batch_size: 12 - eval_batch_size: 8 - GPU: H100 80GB SXM5 - num_devices: 1 - optimizer: adamw_bnb_8bit - lr_scheduler_warmup_steps: 100 - num_epochs: 4 </details> ### Peft hyperparameters <details> <summary>Click to see details</summary> - adapter: qlora - lora_r: 128 - lora_alpha: 256 - lora_dropout: 0.05 - lora_target_linear: true -lora_target_modules: - q_proj - v_proj - k_proj - o_proj - gate_proj - down_proj - up_proj </details> ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.1 - Axolotl - Lm harness for evaluation # Benchmark Results 🔥 OpenBioLLM-8B demonstrates superior performance compared to larger models, such as GPT-3.5, Meditron-70B across 9 diverse biomedical datasets, achieving state-of-the-art results with an average score of 72.50%, despite having a significantly smaller parameter count. The model's strong performance in domain-specific tasks, such as Clinical KG, Medical Genetics, and PubMedQA, highlights its ability to effectively capture and apply biomedical knowledge. 🚨 The GPT-4, Med-PaLM-1, and Med-PaLM-2 results are taken from their official papers. Since Med-PaLM doesn't provide zero-shot accuracy, we are using 5-shot accuracy from their paper for comparison. All results presented are in the zero-shot setting, except for Med-PaLM-2 and Med-PaLM-1, which use 5-shot accuracy. \| \| Clinical KG \| Medical Genetics \| Anatomy \| Pro Medicine \| College Biology \| College Medicine \| MedQA 4 opts \| PubMedQA \| MedMCQA \| Avg \| \|--------------------\|-------------\|------------------\|---------\|--------------\|-----------------\|------------------\|--------------\|----------\|---------\|-------\| \| OpenBioLLM-70B \| 92.93 \| 93.197 \| 83.904 \| 93.75 \| 93.827 \| 85.749 \| 78.162 \| 78.97 \| 74.014 \| 86.05588 \| \| Med-PaLM-2 (5-shot) \| 88.3 \| 90 \| 77.8 \| 95.2 \| 94.4 \| 80.9 \| 79.7 \| 79.2 \| 71.3 \| 84.08 \| \| GPT-4 \| 86.04 \| 91 \| 80 \| 93.01 \| 95.14 \| 76.88 \| 78.87 \| 75.2 \| 69.52 \| 82.85 \| \| Med-PaLM-1 (Flan-PaLM, 5-shot) \| 80.4 \| 75 \| 63.7 \| 83.8 \| 88.9 \| 76.3 \| 67.6 \| 79 \| 57.6 \| 74.7 \| \| OpenBioLLM-8B \| 76.101 \| 86.1 \| 69.829 \| 78.21 \| 84.213 \| 68.042 \| 58.993 \| 74.12 \| 56.913 \| 72.502 \| \| Gemini-1.0 \| 76.7 \| 75.8 \| 66.7 \| 77.7 \| 88 \| 69.2 \| 58 \| 70.7 \| 54.3 \| 70.79 \| \| GPT-3.5 Turbo 1106 \| 74.71 \| 74 \| 72.79 \| 72.79 \| 72.91 \| 64.73 \| 57.71 \| 72.66 \| 53.79 \| 66 \| \| Meditron-70B \| 66.79 \| 69 \| 53.33 \| 71.69 \| 76.38 \| 63 \| 57.1 \| 76.6 \| 46.85 \| 64.52 \| \| gemma-7b \| 69.81 \| 70 \| 59.26 \| 66.18 \| 79.86 \| 60.12 \| 47.21 \| 76.2 \| 48.96 \| 64.18 \| \| Mistral-7B-v0.1 \| 68.68 \| 71 \| 55.56 \| 68.38 \| 68.06 \| 59.54 \| 50.82 \| 75.4 \| 48.2 \| 62.85 \| \| Apollo-7B \| 62.26 \| 72 \| 61.48 \| 69.12 \| 70.83 \| 55.49 \| 55.22 \| 39.8 \| 53.77 \| 60 \| \| MedAlpaca-7b \| 57.36 \| 69 \| 57.04 \| 67.28 \| 65.28 \| 54.34 \| 41.71 \| 72.8 \| 37.51 \| 58.03 \| \| BioMistral-7B \| 59.9 \| 64 \| 56.5 \| 60.4 \| 59 \| 54.7 \| 50.6 \| 77.5 \| 48.1 \| 57.3 \| \| AlpaCare-llama2-7b \| 49.81 \| 49 \| 45.92 \| 33.82 \| 50 \| 43.35 \| 29.77 \| 72.2 \| 34.42 \| 45.36 \| \| ClinicalGPT \| 30.56 \| 27 \| 30.37 \| 19.48 \| 25 \| 24.27 \| 26.08 \| 63.8 \| 28.18 \| 30.52 \| <div align="center"> <img width="1600px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/_SzdcJSBjZyo8RS1bTEkP.png"> </div> ## Detailed Medical Subjectwise accuracy ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/UXF-V0col0Z0sS6BGPBkE.png) # Use Cases & Examples 🚨 Below results are from the quantized version of OpenBioLLM-70B # Summarize Clinical Notes OpenBioLLM-70B can efficiently analyze and summarize complex clinical notes, EHR data, and discharge summaries, extracting key information and generating concise, structured summaries ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/xdwdBgOxNi_TfML0hKlI8.png) # Answer Medical Questions OpenBioLLM-70B can provide answers to a wide range of medical questions. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/zO95GlwOQEZqCKQF69mE6.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/OKBczKw7gWeW5xsuDpc27.png) <details> <summary>Click to see details</summary> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/eJGHT5khppYvJb8fQ-YW4.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/Cnbwrqa_-ORHRuNRC2P6Y.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/J9DhdcvukAc9mnnW9fj2C.png) </details> # Clinical Entity Recognition OpenBioLLM-70B can perform advanced clinical entity recognition by identifying and extracting key medical concepts, such as diseases, symptoms, medications, procedures, and anatomical structures, from unstructured clinical text. By leveraging its deep understanding of medical terminology and context, the model can accurately annotate and categorize clinical entities, enabling more efficient information retrieval, data analysis, and knowledge discovery from electronic health records, research articles, and other biomedical text sources. This capability can support various downstream applications, such as clinical decision support, pharmacovigilance, and medical research. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/_69BW4k9LVABFwtxixL45.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/DKy5wYCoPhoPPUc1-x8_J.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/7WD9zCCBZT4-4XlfnIQjl.png) # Biomarkers Extraction ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/ZttoM4AiteT7gFYVhjIpN.png) # Classification OpenBioLLM-70B can perform various biomedical classification tasks, such as disease prediction, sentiment analysis, medical document categorization ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/Bf5MW1d75qT-1F_TR_hC0.png) # De-Identification OpenBioLLM-70B can detect and remove personally identifiable information (PII) from medical records, ensuring patient privacy and compliance with data protection regulations like HIPAA. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/hKX4kzm--Tw5bj6K78msy.png) Advisory Notice! While OpenBioLLM-70B & 8B leverages high-quality data sources, its outputs may still contain inaccuracies, biases, or misalignments that could pose risks if relied upon for medical decision-making without further testing and refinement. The model's performance has not yet been rigorously evaluated in randomized controlled trials or real-world healthcare environments. Therefore, we strongly advise against using OpenBioLLM-70B & 8B for any direct patient care, clinical decision support, or other professional medical purposes at this time. Its use should be limited to research, development, and exploratory applications by qualified individuals who understand its limitations. OpenBioLLM-70B & 8B are intended solely as a research tool to assist healthcare professionals and should never be considered a replacement for the professional judgment and expertise of a qualified medical doctor. Appropriately adapting and validating OpenBioLLM-70B & 8B for specific medical use cases would require significant additional work, potentially including: - Thorough testing and evaluation in relevant clinical scenarios - Alignment with evidence-based guidelines and best practices - Mitigation of potential biases and failure modes - Integration with human oversight and interpretation - Compliance with regulatory and ethical standards Always consult a qualified healthcare provider for personal medical needs. # Citation If you find OpenBioLLM-70B & 8B useful in your work, please cite the model as follows: ``` @misc{OpenBioLLMs, author = {Ankit Pal, Malaikannan Sankarasubbu}, title = {OpenBioLLMs: Advancing Open-Source Large Language Models for Healthcare and Life Sciences}, year = {2024}, publisher = {Hugging Face}, journal = {Hugging Face repository}, howpublished = {\url{https://huggingface.co/aaditya/OpenBioLLM-Llama3-70B}} } ``` The accompanying paper is currently in progress and will be released soon. <div align="center"> <h2> 💌 Contact </h2> </div> We look forward to hearing you and collaborating on this exciting project! Contributors: - [Ankit Pal (Aaditya Ura)](https://aadityaura.github.io/) [aadityaura at gmail dot com] - Saama AI Labs - Note: I am looking for a funded PhD opportunity, especially if it fits my Responsible Generative AI, Multimodal LLMs, Geometric Deep Learning, and Healthcare AI skillset. # References We thank the [Meta Team](meta-llama/Meta-Llama-3-70B-Instruct) for their amazing models! Result sources - [1] GPT-4 [Capabilities of GPT-4 on Medical Challenge Problems] (https://arxiv.org/abs/2303.13375) - [2] Med-PaLM-1 [Large Language Models Encode Clinical Knowledge](https://arxiv.org/abs/2212.13138) - [3] Med-PaLM-2 [Towards Expert-Level Medical Question Answering with Large Language Models](https://arxiv.org/abs/2305.09617) - [4] Gemini-1.0 [Gemini Goes to Med School](https://arxiv.org/abs/2402.07023)	{"language": ["en"], "license": "llama3", "tags": ["llama-3", "llama", "Mixtral", "instruct", "finetune", "chatml", "DPO", "RLHF", "gpt4", "distillation"], "base_model": "meta-llama/Meta-Llama-3-8B", "widget": [{"example_title": "OpenBioLLM-8B", "messages": [{"role": "system", "content": "You are an expert and experienced from the healthcare and biomedical domain with extensive medical knowledge and practical experience."}, {"role": "user", "content": "How long does it take for newborn jaundice to go away?"}], "output": {"text": "Newborn jaundice, also known as neonatal jaundice, is a common condition in newborns where the yellowing of the skin and eyes occurs due to an elevated level of bilirubin in the blood. Bilirubin is a yellow pigment that forms when red blood cells break down. In most cases, newborn jaundice resolves on its own without any specific treatment.\nThe duration of newborn jaundice can vary depending on several factors such as the underlying cause, gestational age at birth, and individual variations in bilirubin metabolism. Here are some general guidelines:\n1. Physiological jaundice: This is the most common type of newborn jaundice and usually appears within 24-72 hours after birth. It tends to peak between the second and fifth day of life and gradually improves over the next week or two. By the time the baby is one week old, the jaundice should have mostly resolved. 2. Breast milk jaundice: This type of jaundice occurs in breastfed babies and may appear later than physiological jaundice, typically between the fifth and fourteenth day of life. It tends to persist for a longer duration but usually resolves within six weeks after birth. 3. Pathological jaundice: This type of jaundice is less common and occurs due to an underlying medical condition that affects bilirubin metabolism or liver function. The duration of pathological jaundice depends on the specific cause and may require treatment.\nIt's important for parents to monitor their newborn's jaundice closely and seek medical advice if the jaundice progresses rapidly, becomes severe, or is accompanied by other symptoms such as poor feeding, lethargy, or excessive sleepiness. In these cases, further evaluation and management may be necessary. Remember that each baby is unique, and the timing of jaundice resolution can vary. If you have concerns about your newborn's jaundice, it's always best to consult with a healthcare professional for personalized advice and guidance."}}], "model-index": [{"name": "OpenBioLLM-8B", "results": []}]}	LoneStriker/OpenBioLLM-Llama3-8B-4.0bpw-h6-exl2	null	[ "transformers", "pytorch", "llama", "text-generation", "llama-3", "Mixtral", "instruct", "finetune", "chatml", "DPO", "RLHF", "gpt4", "distillation", "en", "arxiv:2305.18290", "arxiv:2303.13375", "arxiv:2212.13138", "arxiv:2305.09617", "arxiv:2402.07023", "base_model:meta-llama/Meta-Llama-3-8B", "license:llama3", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]	null	2024-04-26T19:42:43+00:00	[ "2305.18290", "2303.13375", "2212.13138", "2305.09617", "2402.07023" ]	[ "en" ]	TAGS #transformers #pytorch #llama #text-generation #llama-3 #Mixtral #instruct #finetune #chatml #DPO #RLHF #gpt4 #distillation #en #arxiv-2305.18290 #arxiv-2303.13375 #arxiv-2212.13138 #arxiv-2305.09617 #arxiv-2402.07023 #base_model-meta-llama/Meta-Llama-3-8B #license-llama3 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us	![](URL <p>!image/png</p> <div align=) Advancing Open-source Large Language Models in Medical Domain ============================================================= Online Demo \| GitHub \| [![](URL alt=)](#) \| Discord !image/jpeg Introducing OpenBioLLM-8B: A State-of-the-Art Open Source Biomedical Large Language Model OpenBioLLM-8B is an advanced open source language model designed specifically for the biomedical domain. Developed by Saama AI Labs, this model leverages cutting-edge techniques to achieve state-of-the-art performance on a wide range of biomedical tasks. Biomedical Specialization: OpenBioLLM-8B is tailored for the unique language and knowledge requirements of the medical and life sciences fields. It was fine-tuned on a vast corpus of high-quality biomedical data, enabling it to understand and generate text with domain-specific accuracy and fluency. Superior Performance: With 8 billion parameters, OpenBioLLM-8B outperforms other open source biomedical language models of similar scale. It has also demonstrated better results compared to larger proprietary & open-source models like GPT-3.5 and Meditron-70B on biomedical benchmarks. Advanced Training Techniques: OpenBioLLM-8B builds upon the powerful foundations of the Meta-Llama-3-8B and Meta-Llama-3-8B models. It incorporates the DPO dataset and fine-tuning recipe along with a custom diverse medical instruction dataset. Key components of the training pipeline include: ![](URL </div> <ul> <li>Policy Optimization: Direct Preference Optimization: Your Language Model is Secretly a Reward Model (DPO)</li> <li>Ranking Dataset: berkeley-nest/Nectar</li> <li>Fine-tuning dataset: Custom Medical Instruct dataset (We plan to release a sample training dataset in our upcoming paper; please stay updated)</li> </ul> <p>This combination of cutting-edge techniques enables OpenBioLLM-8B to align with key capabilities and preferences for biomedical applications.</p> <p>️ Release Details:</p> <ul> <li>Model Size: 8 billion parameters</li> <li>Quantization: Optimized quantized versions available Here</li> <li>Language(s) (NLP): en</li> <li>Developed By: Ankit Pal (Aaditya Ura) from Saama AI Labs</li> <li>License: Meta-Llama License</li> <li>Fine-tuned from models: meta-llama/Meta-Llama-3-8B</li> <li>Resources for more information: <ul> <li>Paper: Coming soon</li> </ul> </li> </ul> <p>The model can be fine-tuned for more specialized tasks and datasets as needed.</p> <p>OpenBioLLM-8B represents an important step forward in democratizing advanced language AI for the biomedical community. By leveraging state-of-the-art architectures and training techniques from leading open source efforts like Llama-3, we have created a powerful tool to accelerate innovation and discovery in healthcare and the life sciences.</p> <p>We are excited to share OpenBioLLM-8B with researchers and developers around the world.</p> <h3>Use with transformers</h3> <p>Important: Please use the exact chat template provided by Llama-3 instruct version. Otherwise there will be a degradation in the performance. The model output can be verbose in rare cases. Please consider setting temperature = 0 to make this happen less.</p> <p>See the snippet below for usage with Transformers:</p> <h2>Training procedure</h2> <h3>Training hyperparameters</h3> <details> <summary>Click to see details</summary> <ul> <li>learning_rate: 0.0002</li> <li>lr_scheduler: cosine</li> <li>train_batch_size: 12</li> <li>eval_batch_size: 8</li> <li>GPU: H100 80GB SXM5</li> <li>num_devices: 1</li> <li>optimizer: adamw_bnb_8bit</li> <li>lr_scheduler_warmup_steps: 100</li> <li>num_epochs: 4</li> </ul> </details> <h3>Peft hyperparameters</h3> <details> <summary>Click to see details</summary> <ul> <li>adapter: qlora</li> <li>lora_r: 128</li> <li>lora_alpha: 256</li> <li>lora_dropout: 0.05</li> <li>lora_target_linear: true</li> </ul> <p>-lora_target_modules:</p> <ul> <li>q_proj</li> <li>v_proj</li> <li>k_proj</li> <li>o_proj</li> <li>gate_proj</li> <li>down_proj</li> <li>up_proj</li> </ul> </details> <h3>Training results</h3> <h3>Framework versions</h3> <ul> <li>Transformers 4.39.3</li> <li>Pytorch 2.1.2+cu121</li> <li>Datasets 2.18.0</li> <li>Tokenizers 0.15.1</li> <li>Axolotl</li> <li>Lm harness for evaluation</li> </ul> <h1>Benchmark Results</h1> <p>OpenBioLLM-8B demonstrates superior performance compared to larger models, such as GPT-3.5, Meditron-70B across 9 diverse biomedical datasets, achieving state-of-the-art results with an average score of 72.50%, despite having a significantly smaller parameter count. The model's strong performance in domain-specific tasks, such as Clinical KG, Medical Genetics, and PubMedQA, highlights its ability to effectively capture and apply biomedical knowledge.</p> <p>The GPT-4, Med-PaLM-1, and Med-PaLM-2 results are taken from their official papers. Since Med-PaLM doesn't provide zero-shot accuracy, we are using 5-shot accuracy from their paper for comparison. All results presented are in the zero-shot setting, except for Med-PaLM-2 and Med-PaLM-1, which use 5-shot accuracy.</p> <p></p> <div align=) ![](URL </div> <h2>Detailed Medical Subjectwise accuracy</h2> <p>!image/png</p> <h1>Use Cases & Examples</h1> <p>Below results are from the quantized version of OpenBioLLM-70B</p> <h1>Summarize Clinical Notes</h1> <p>OpenBioLLM-70B can efficiently analyze and summarize complex clinical notes, EHR data, and discharge summaries, extracting key information and generating concise, structured summaries</p> <p>!image/png</p> <h1>Answer Medical Questions</h1> <p>OpenBioLLM-70B can provide answers to a wide range of medical questions.</p> <p>!image/png !image/png</p> <details> <summary>Click to see details</summary> <p>!image/png !image/png !image/png</p> </details> <h1>Clinical Entity Recognition</h1> <p>OpenBioLLM-70B can perform advanced clinical entity recognition by identifying and extracting key medical concepts, such as diseases, symptoms, medications, procedures, and anatomical structures, from unstructured clinical text. By leveraging its deep understanding of medical terminology and context, the model can accurately annotate and categorize clinical entities, enabling more efficient information retrieval, data analysis, and knowledge discovery from electronic health records, research articles, and other biomedical text sources. This capability can support various downstream applications, such as clinical decision support, pharmacovigilance, and medical research.</p> <p>!image/png !image/png !image/png</p> <h1>Biomarkers Extraction</h1> <p>!image/png</p> <h1>Classification</h1> <p>OpenBioLLM-70B can perform various biomedical classification tasks, such as disease prediction, sentiment analysis, medical document categorization</p> <p>!image/png</p> <h1>De-Identification</h1> <p>OpenBioLLM-70B can detect and remove personally identifiable information (PII) from medical records, ensuring patient privacy and compliance with data protection regulations like HIPAA.</p> <p>!image/png</p> <p>Advisory Notice!</p> <p>While OpenBioLLM-70B & 8B leverages high-quality data sources, its outputs may still contain inaccuracies, biases, or misalignments that could pose risks if relied upon for medical decision-making without further testing and refinement. The model's performance has not yet been rigorously evaluated in randomized controlled trials or real-world healthcare environments.</p> <p>Therefore, we strongly advise against using OpenBioLLM-70B & 8B for any direct patient care, clinical decision support, or other professional medical purposes at this time. Its use should be limited to research, development, and exploratory applications by qualified individuals who understand its limitations. OpenBioLLM-70B & 8B are intended solely as a research tool to assist healthcare professionals and should never be considered a replacement for the professional judgment and expertise of a qualified medical doctor.</p> <p>Appropriately adapting and validating OpenBioLLM-70B & 8B for specific medical use cases would require significant additional work, potentially including:</p> <ul> <li>Thorough testing and evaluation in relevant clinical scenarios</li> <li>Alignment with evidence-based guidelines and best practices</li> <li>Mitigation of potential biases and failure modes</li> <li>Integration with human oversight and interpretation</li> <li>Compliance with regulatory and ethical standards</li> </ul> <p>Always consult a qualified healthcare provider for personal medical needs.</p> <p>If you find OpenBioLLM-70B & 8B useful in your work, please cite the model as follows:</p> <p>The accompanying paper is currently in progress and will be released soon.</p> <div align=) Contact -------- We look forward to hearing you and collaborating on this exciting project! Contributors: * Ankit Pal (Aaditya Ura) [aadityaura at gmail dot com] * Saama AI Labs * Note: I am looking for a funded PhD opportunity, especially if it fits my Responsible Generative AI, Multimodal LLMs, Geometric Deep Learning, and Healthcare AI skillset. References ========== We thank the Meta Team for their amazing models! Result sources * [1] GPT-4 [Capabilities of GPT-4 on Medical Challenge Problems] (URL * [2] Med-PaLM-1 Large Language Models Encode Clinical Knowledge * [3] Med-PaLM-2 Towards Expert-Level Medical Question Answering with Large Language Models * [4] Gemini-1.0 Gemini Goes to Med School	[]	[ "TAGS\n#transformers #pytorch #llama #text-generation #llama-3 #Mixtral #instruct #finetune #chatml #DPO #RLHF #gpt4 #distillation #en #arxiv-2305.18290 #arxiv-2303.13375 #arxiv-2212.13138 #arxiv-2305.09617 #arxiv-2402.07023 #base_model-meta-llama/Meta-Llama-3-8B #license-llama3 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n" ]
text-generation	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 0.001_4iters_bs128_nodpo_only4w_userresponse_iter_1 This model is a fine-tuned version of [HuggingFaceH4/mistral-7b-sft-beta](https://huggingface.co/HuggingFaceH4/mistral-7b-sft-beta) on the updated and the original datasets. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.40.0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.19.1	{"license": "mit", "tags": ["alignment-handbook", "trl", "dpo", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["updated", "original"], "base_model": "HuggingFaceH4/mistral-7b-sft-beta", "model-index": [{"name": "0.001_4iters_bs128_nodpo_only4w_userresponse_iter_1", "results": []}]}	ShenaoZhang/0.001_4iters_bs128_nodpo_only4w_userresponse_iter_1	null	[ "transformers", "safetensors", "mistral", "text-generation", "alignment-handbook", "trl", "dpo", "generated_from_trainer", "conversational", "dataset:updated", "dataset:original", "base_model:HuggingFaceH4/mistral-7b-sft-beta", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-26T19:44:03+00:00	[]	[]	TAGS #transformers #safetensors #mistral #text-generation #alignment-handbook #trl #dpo #generated_from_trainer #conversational #dataset-updated #dataset-original #base_model-HuggingFaceH4/mistral-7b-sft-beta #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# 0.001_4iters_bs128_nodpo_only4w_userresponse_iter_1 This model is a fine-tuned version of HuggingFaceH4/mistral-7b-sft-beta on the updated and the original datasets. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.40.0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.19.1	[ "# 0.001_4iters_bs128_nodpo_only4w_userresponse_iter_1\n\nThis model is a fine-tuned version of HuggingFaceH4/mistral-7b-sft-beta on the updated and the original datasets.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 128\n- total_eval_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.40.0\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.19.1" ]	[ "TAGS\n#transformers #safetensors #mistral #text-generation #alignment-handbook #trl #dpo #generated_from_trainer #conversational #dataset-updated #dataset-original #base_model-HuggingFaceH4/mistral-7b-sft-beta #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# 0.001_4iters_bs128_nodpo_only4w_userresponse_iter_1\n\nThis model is a fine-tuned version of HuggingFaceH4/mistral-7b-sft-beta on the updated and the original datasets.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 128\n- total_eval_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.40.0\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.19.1" ]
null	transformers	# Uploaded model - Developed by: ogdanneedham - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl"], "base_model": "unsloth/mistral-7b-instruct-v0.2-bnb-4bit"}	ogdanneedham/mistral-sf-64-lora	null	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-26T19:44:41+00:00	[]	[ "en" ]	TAGS #transformers #safetensors #text-generation-inference #unsloth #mistral #trl #en #base_model-unsloth/mistral-7b-instruct-v0.2-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us	# Uploaded model - Developed by: ogdanneedham - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library. <img src="URL width="200"/>	[ "# Uploaded model\n\n- Developed by: ogdanneedham\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]	[ "TAGS\n#transformers #safetensors #text-generation-inference #unsloth #mistral #trl #en #base_model-unsloth/mistral-7b-instruct-v0.2-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n", "# Uploaded model\n\n- Developed by: ogdanneedham\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]
text-generation	transformers	<div align="center"> <img width="260px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/BrQCb95lmEIFz79QAmoNA.png"></div> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/2FhDh8NDvMl7iSxbQz9BP.png) <div align="center"> <h1>Advancing Open-source Large Language Models in Medical Domain</h1> </div> <p align="center" style="margin-top: 0px;"> <a href="https://colab.research.google.com/drive/1F5oV20InEYeAJGmBwYF9NM_QhLmjBkKJ?usp=sharing"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="OpenChat Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 10px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">Online Demo</span> </a> \| <a href="https://github.com/openlifescience-ai"> <img src="https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png" alt="GitHub Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">GitHub</span> </a> \| <a href="#"> <img src="https://github.com/alpayariyak/openchat/blob/master/assets/arxiv-logomark-small-square-border.png?raw=true" alt="ArXiv Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style="margin-right: 5px;">Paper</span> </a> \| <a href="https://discord.gg/A5Fjf5zC69"> <img src="https://cloud.githubusercontent.com/assets/6291467/26705903/96c2d66e-477c-11e7-9f4e-f3c0efe96c9a.png" alt="Discord Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text">Discord</span> </a> </p> ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/KGmRE5w2sepNtwsEu8t7K.jpeg) Introducing OpenBioLLM-8B: A State-of-the-Art Open Source Biomedical Large Language Model OpenBioLLM-8B is an advanced open source language model designed specifically for the biomedical domain. Developed by Saama AI Labs, this model leverages cutting-edge techniques to achieve state-of-the-art performance on a wide range of biomedical tasks. 🏥 Biomedical Specialization: OpenBioLLM-8B is tailored for the unique language and knowledge requirements of the medical and life sciences fields. It was fine-tuned on a vast corpus of high-quality biomedical data, enabling it to understand and generate text with domain-specific accuracy and fluency. 🎓 Superior Performance: With 8 billion parameters, OpenBioLLM-8B outperforms other open source biomedical language models of similar scale. It has also demonstrated better results compared to larger proprietary & open-source models like GPT-3.5 and Meditron-70B on biomedical benchmarks. 🧠 Advanced Training Techniques: OpenBioLLM-8B builds upon the powerful foundations of the Meta-Llama-3-8B and [Meta-Llama-3-8B](meta-llama/Meta-Llama-3-8B) models. It incorporates the DPO dataset and fine-tuning recipe along with a custom diverse medical instruction dataset. Key components of the training pipeline include: <div align="center"> <img width="1200px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/oPchsJsEpQoGcGXVbh7YS.png"> </div> - Policy Optimization: [Direct Preference Optimization: Your Language Model is Secretly a Reward Model (DPO)](https://arxiv.org/abs/2305.18290) - Ranking Dataset: [berkeley-nest/Nectar](https://huggingface.co/datasets/berkeley-nest/Nectar) - Fine-tuning dataset: Custom Medical Instruct dataset (We plan to release a sample training dataset in our upcoming paper; please stay updated) This combination of cutting-edge techniques enables OpenBioLLM-8B to align with key capabilities and preferences for biomedical applications. ⚙️ Release Details: - Model Size: 8 billion parameters - Quantization: Optimized quantized versions available [Here](https://huggingface.co/aaditya/OpenBioLLM-8B-GGUF) - Language(s) (NLP): en - Developed By: [Ankit Pal (Aaditya Ura)](https://aadityaura.github.io/) from Saama AI Labs - License: Meta-Llama License - Fine-tuned from models: [meta-llama/Meta-Llama-3-8B](meta-llama/Meta-Llama-3-8B) - Resources for more information: - Paper: Coming soon The model can be fine-tuned for more specialized tasks and datasets as needed. OpenBioLLM-8B represents an important step forward in democratizing advanced language AI for the biomedical community. By leveraging state-of-the-art architectures and training techniques from leading open source efforts like Llama-3, we have created a powerful tool to accelerate innovation and discovery in healthcare and the life sciences. We are excited to share OpenBioLLM-8B with researchers and developers around the world. ### Use with transformers Important: Please use the exact chat template provided by Llama-3 instruct version. Otherwise there will be a degradation in the performance. The model output can be verbose in rare cases. Please consider setting temperature = 0 to make this happen less. See the snippet below for usage with Transformers: ```python import transformers import torch model_id = "aaditya/OpenBioLLM-Llama3-8B" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device="auto", ) messages = [ {"role": "system", "content": "You are an expert and experienced from the healthcare and biomedical domain with extensive medical knowledge and practical experience. Your name is OpenBioLLM, and you were developed by Saama AI Labs. who's willing to help answer the user's query with explanation. In your explanation, leverage your deep medical expertise such as relevant anatomical structures, physiological processes, diagnostic criteria, treatment guidelines, or other pertinent medical concepts. Use precise medical terminology while still aiming to make the explanation clear and accessible to a general audience."}, {"role": "user", "content": "How can i split a 3mg or 4mg waefin pill so i can get a 2.5mg pill?"}, ] prompt = pipeline.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) terminators = [ pipeline.tokenizer.eos_token_id, pipeline.tokenizer.convert_tokens_to_ids("<\|eot_id\|>") ] outputs = pipeline( prompt, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.0, top_p=0.9, ) print(outputs[0]["generated_text"][len(prompt):]) ``` ## Training procedure ### Training hyperparameters <details> <summary>Click to see details</summary> - learning_rate: 0.0002 - lr_scheduler: cosine - train_batch_size: 12 - eval_batch_size: 8 - GPU: H100 80GB SXM5 - num_devices: 1 - optimizer: adamw_bnb_8bit - lr_scheduler_warmup_steps: 100 - num_epochs: 4 </details> ### Peft hyperparameters <details> <summary>Click to see details</summary> - adapter: qlora - lora_r: 128 - lora_alpha: 256 - lora_dropout: 0.05 - lora_target_linear: true -lora_target_modules: - q_proj - v_proj - k_proj - o_proj - gate_proj - down_proj - up_proj </details> ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.1 - Axolotl - Lm harness for evaluation # Benchmark Results 🔥 OpenBioLLM-8B demonstrates superior performance compared to larger models, such as GPT-3.5, Meditron-70B across 9 diverse biomedical datasets, achieving state-of-the-art results with an average score of 72.50%, despite having a significantly smaller parameter count. The model's strong performance in domain-specific tasks, such as Clinical KG, Medical Genetics, and PubMedQA, highlights its ability to effectively capture and apply biomedical knowledge. 🚨 The GPT-4, Med-PaLM-1, and Med-PaLM-2 results are taken from their official papers. Since Med-PaLM doesn't provide zero-shot accuracy, we are using 5-shot accuracy from their paper for comparison. All results presented are in the zero-shot setting, except for Med-PaLM-2 and Med-PaLM-1, which use 5-shot accuracy. \| \| Clinical KG \| Medical Genetics \| Anatomy \| Pro Medicine \| College Biology \| College Medicine \| MedQA 4 opts \| PubMedQA \| MedMCQA \| Avg \| \|--------------------\|-------------\|------------------\|---------\|--------------\|-----------------\|------------------\|--------------\|----------\|---------\|-------\| \| OpenBioLLM-70B \| 92.93 \| 93.197 \| 83.904 \| 93.75 \| 93.827 \| 85.749 \| 78.162 \| 78.97 \| 74.014 \| 86.05588 \| \| Med-PaLM-2 (5-shot) \| 88.3 \| 90 \| 77.8 \| 95.2 \| 94.4 \| 80.9 \| 79.7 \| 79.2 \| 71.3 \| 84.08 \| \| GPT-4 \| 86.04 \| 91 \| 80 \| 93.01 \| 95.14 \| 76.88 \| 78.87 \| 75.2 \| 69.52 \| 82.85 \| \| Med-PaLM-1 (Flan-PaLM, 5-shot) \| 80.4 \| 75 \| 63.7 \| 83.8 \| 88.9 \| 76.3 \| 67.6 \| 79 \| 57.6 \| 74.7 \| \| OpenBioLLM-8B \| 76.101 \| 86.1 \| 69.829 \| 78.21 \| 84.213 \| 68.042 \| 58.993 \| 74.12 \| 56.913 \| 72.502 \| \| Gemini-1.0 \| 76.7 \| 75.8 \| 66.7 \| 77.7 \| 88 \| 69.2 \| 58 \| 70.7 \| 54.3 \| 70.79 \| \| GPT-3.5 Turbo 1106 \| 74.71 \| 74 \| 72.79 \| 72.79 \| 72.91 \| 64.73 \| 57.71 \| 72.66 \| 53.79 \| 66 \| \| Meditron-70B \| 66.79 \| 69 \| 53.33 \| 71.69 \| 76.38 \| 63 \| 57.1 \| 76.6 \| 46.85 \| 64.52 \| \| gemma-7b \| 69.81 \| 70 \| 59.26 \| 66.18 \| 79.86 \| 60.12 \| 47.21 \| 76.2 \| 48.96 \| 64.18 \| \| Mistral-7B-v0.1 \| 68.68 \| 71 \| 55.56 \| 68.38 \| 68.06 \| 59.54 \| 50.82 \| 75.4 \| 48.2 \| 62.85 \| \| Apollo-7B \| 62.26 \| 72 \| 61.48 \| 69.12 \| 70.83 \| 55.49 \| 55.22 \| 39.8 \| 53.77 \| 60 \| \| MedAlpaca-7b \| 57.36 \| 69 \| 57.04 \| 67.28 \| 65.28 \| 54.34 \| 41.71 \| 72.8 \| 37.51 \| 58.03 \| \| BioMistral-7B \| 59.9 \| 64 \| 56.5 \| 60.4 \| 59 \| 54.7 \| 50.6 \| 77.5 \| 48.1 \| 57.3 \| \| AlpaCare-llama2-7b \| 49.81 \| 49 \| 45.92 \| 33.82 \| 50 \| 43.35 \| 29.77 \| 72.2 \| 34.42 \| 45.36 \| \| ClinicalGPT \| 30.56 \| 27 \| 30.37 \| 19.48 \| 25 \| 24.27 \| 26.08 \| 63.8 \| 28.18 \| 30.52 \| <div align="center"> <img width="1600px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/_SzdcJSBjZyo8RS1bTEkP.png"> </div> ## Detailed Medical Subjectwise accuracy ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/UXF-V0col0Z0sS6BGPBkE.png) # Use Cases & Examples 🚨 Below results are from the quantized version of OpenBioLLM-70B # Summarize Clinical Notes OpenBioLLM-70B can efficiently analyze and summarize complex clinical notes, EHR data, and discharge summaries, extracting key information and generating concise, structured summaries ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/xdwdBgOxNi_TfML0hKlI8.png) # Answer Medical Questions OpenBioLLM-70B can provide answers to a wide range of medical questions. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/zO95GlwOQEZqCKQF69mE6.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/OKBczKw7gWeW5xsuDpc27.png) <details> <summary>Click to see details</summary> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/eJGHT5khppYvJb8fQ-YW4.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/Cnbwrqa_-ORHRuNRC2P6Y.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/J9DhdcvukAc9mnnW9fj2C.png) </details> # Clinical Entity Recognition OpenBioLLM-70B can perform advanced clinical entity recognition by identifying and extracting key medical concepts, such as diseases, symptoms, medications, procedures, and anatomical structures, from unstructured clinical text. By leveraging its deep understanding of medical terminology and context, the model can accurately annotate and categorize clinical entities, enabling more efficient information retrieval, data analysis, and knowledge discovery from electronic health records, research articles, and other biomedical text sources. This capability can support various downstream applications, such as clinical decision support, pharmacovigilance, and medical research. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/_69BW4k9LVABFwtxixL45.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/DKy5wYCoPhoPPUc1-x8_J.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/7WD9zCCBZT4-4XlfnIQjl.png) # Biomarkers Extraction ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/ZttoM4AiteT7gFYVhjIpN.png) # Classification OpenBioLLM-70B can perform various biomedical classification tasks, such as disease prediction, sentiment analysis, medical document categorization ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/Bf5MW1d75qT-1F_TR_hC0.png) # De-Identification OpenBioLLM-70B can detect and remove personally identifiable information (PII) from medical records, ensuring patient privacy and compliance with data protection regulations like HIPAA. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/hKX4kzm--Tw5bj6K78msy.png) Advisory Notice! While OpenBioLLM-70B & 8B leverages high-quality data sources, its outputs may still contain inaccuracies, biases, or misalignments that could pose risks if relied upon for medical decision-making without further testing and refinement. The model's performance has not yet been rigorously evaluated in randomized controlled trials or real-world healthcare environments. Therefore, we strongly advise against using OpenBioLLM-70B & 8B for any direct patient care, clinical decision support, or other professional medical purposes at this time. Its use should be limited to research, development, and exploratory applications by qualified individuals who understand its limitations. OpenBioLLM-70B & 8B are intended solely as a research tool to assist healthcare professionals and should never be considered a replacement for the professional judgment and expertise of a qualified medical doctor. Appropriately adapting and validating OpenBioLLM-70B & 8B for specific medical use cases would require significant additional work, potentially including: - Thorough testing and evaluation in relevant clinical scenarios - Alignment with evidence-based guidelines and best practices - Mitigation of potential biases and failure modes - Integration with human oversight and interpretation - Compliance with regulatory and ethical standards Always consult a qualified healthcare provider for personal medical needs. # Citation If you find OpenBioLLM-70B & 8B useful in your work, please cite the model as follows: ``` @misc{OpenBioLLMs, author = {Ankit Pal, Malaikannan Sankarasubbu}, title = {OpenBioLLMs: Advancing Open-Source Large Language Models for Healthcare and Life Sciences}, year = {2024}, publisher = {Hugging Face}, journal = {Hugging Face repository}, howpublished = {\url{https://huggingface.co/aaditya/OpenBioLLM-Llama3-70B}} } ``` The accompanying paper is currently in progress and will be released soon. <div align="center"> <h2> 💌 Contact </h2> </div> We look forward to hearing you and collaborating on this exciting project! Contributors: - [Ankit Pal (Aaditya Ura)](https://aadityaura.github.io/) [aadityaura at gmail dot com] - Saama AI Labs - Note: I am looking for a funded PhD opportunity, especially if it fits my Responsible Generative AI, Multimodal LLMs, Geometric Deep Learning, and Healthcare AI skillset. # References We thank the [Meta Team](meta-llama/Meta-Llama-3-70B-Instruct) for their amazing models! Result sources - [1] GPT-4 [Capabilities of GPT-4 on Medical Challenge Problems] (https://arxiv.org/abs/2303.13375) - [2] Med-PaLM-1 [Large Language Models Encode Clinical Knowledge](https://arxiv.org/abs/2212.13138) - [3] Med-PaLM-2 [Towards Expert-Level Medical Question Answering with Large Language Models](https://arxiv.org/abs/2305.09617) - [4] Gemini-1.0 [Gemini Goes to Med School](https://arxiv.org/abs/2402.07023)	{"language": ["en"], "license": "llama3", "tags": ["llama-3", "llama", "Mixtral", "instruct", "finetune", "chatml", "DPO", "RLHF", "gpt4", "distillation"], "base_model": "meta-llama/Meta-Llama-3-8B", "widget": [{"example_title": "OpenBioLLM-8B", "messages": [{"role": "system", "content": "You are an expert and experienced from the healthcare and biomedical domain with extensive medical knowledge and practical experience."}, {"role": "user", "content": "How long does it take for newborn jaundice to go away?"}], "output": {"text": "Newborn jaundice, also known as neonatal jaundice, is a common condition in newborns where the yellowing of the skin and eyes occurs due to an elevated level of bilirubin in the blood. Bilirubin is a yellow pigment that forms when red blood cells break down. In most cases, newborn jaundice resolves on its own without any specific treatment.\nThe duration of newborn jaundice can vary depending on several factors such as the underlying cause, gestational age at birth, and individual variations in bilirubin metabolism. Here are some general guidelines:\n1. Physiological jaundice: This is the most common type of newborn jaundice and usually appears within 24-72 hours after birth. It tends to peak between the second and fifth day of life and gradually improves over the next week or two. By the time the baby is one week old, the jaundice should have mostly resolved. 2. Breast milk jaundice: This type of jaundice occurs in breastfed babies and may appear later than physiological jaundice, typically between the fifth and fourteenth day of life. It tends to persist for a longer duration but usually resolves within six weeks after birth. 3. Pathological jaundice: This type of jaundice is less common and occurs due to an underlying medical condition that affects bilirubin metabolism or liver function. The duration of pathological jaundice depends on the specific cause and may require treatment.\nIt's important for parents to monitor their newborn's jaundice closely and seek medical advice if the jaundice progresses rapidly, becomes severe, or is accompanied by other symptoms such as poor feeding, lethargy, or excessive sleepiness. In these cases, further evaluation and management may be necessary. Remember that each baby is unique, and the timing of jaundice resolution can vary. If you have concerns about your newborn's jaundice, it's always best to consult with a healthcare professional for personalized advice and guidance."}}], "model-index": [{"name": "OpenBioLLM-8B", "results": []}]}	LoneStriker/OpenBioLLM-Llama3-8B-5.0bpw-h6-exl2	null	[ "transformers", "pytorch", "llama", "text-generation", "llama-3", "Mixtral", "instruct", "finetune", "chatml", "DPO", "RLHF", "gpt4", "distillation", "en", "arxiv:2305.18290", "arxiv:2303.13375", "arxiv:2212.13138", "arxiv:2305.09617", "arxiv:2402.07023", "base_model:meta-llama/Meta-Llama-3-8B", "license:llama3", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "5-bit", "region:us" ]	null	2024-04-26T19:45:27+00:00	[ "2305.18290", "2303.13375", "2212.13138", "2305.09617", "2402.07023" ]	[ "en" ]	TAGS #transformers #pytorch #llama #text-generation #llama-3 #Mixtral #instruct #finetune #chatml #DPO #RLHF #gpt4 #distillation #en #arxiv-2305.18290 #arxiv-2303.13375 #arxiv-2212.13138 #arxiv-2305.09617 #arxiv-2402.07023 #base_model-meta-llama/Meta-Llama-3-8B #license-llama3 #autotrain_compatible #endpoints_compatible #text-generation-inference #5-bit #region-us	![](URL <p>!image/png</p> <div align=) Advancing Open-source Large Language Models in Medical Domain ============================================================= Online Demo \| GitHub \| [![](URL alt=)](#) \| Discord !image/jpeg Introducing OpenBioLLM-8B: A State-of-the-Art Open Source Biomedical Large Language Model OpenBioLLM-8B is an advanced open source language model designed specifically for the biomedical domain. Developed by Saama AI Labs, this model leverages cutting-edge techniques to achieve state-of-the-art performance on a wide range of biomedical tasks. Biomedical Specialization: OpenBioLLM-8B is tailored for the unique language and knowledge requirements of the medical and life sciences fields. It was fine-tuned on a vast corpus of high-quality biomedical data, enabling it to understand and generate text with domain-specific accuracy and fluency. Superior Performance: With 8 billion parameters, OpenBioLLM-8B outperforms other open source biomedical language models of similar scale. It has also demonstrated better results compared to larger proprietary & open-source models like GPT-3.5 and Meditron-70B on biomedical benchmarks. Advanced Training Techniques: OpenBioLLM-8B builds upon the powerful foundations of the Meta-Llama-3-8B and Meta-Llama-3-8B models. It incorporates the DPO dataset and fine-tuning recipe along with a custom diverse medical instruction dataset. Key components of the training pipeline include: ![](URL </div> <ul> <li>Policy Optimization: Direct Preference Optimization: Your Language Model is Secretly a Reward Model (DPO)</li> <li>Ranking Dataset: berkeley-nest/Nectar</li> <li>Fine-tuning dataset: Custom Medical Instruct dataset (We plan to release a sample training dataset in our upcoming paper; please stay updated)</li> </ul> <p>This combination of cutting-edge techniques enables OpenBioLLM-8B to align with key capabilities and preferences for biomedical applications.</p> <p>️ Release Details:</p> <ul> <li>Model Size: 8 billion parameters</li> <li>Quantization: Optimized quantized versions available Here</li> <li>Language(s) (NLP): en</li> <li>Developed By: Ankit Pal (Aaditya Ura) from Saama AI Labs</li> <li>License: Meta-Llama License</li> <li>Fine-tuned from models: meta-llama/Meta-Llama-3-8B</li> <li>Resources for more information: <ul> <li>Paper: Coming soon</li> </ul> </li> </ul> <p>The model can be fine-tuned for more specialized tasks and datasets as needed.</p> <p>OpenBioLLM-8B represents an important step forward in democratizing advanced language AI for the biomedical community. By leveraging state-of-the-art architectures and training techniques from leading open source efforts like Llama-3, we have created a powerful tool to accelerate innovation and discovery in healthcare and the life sciences.</p> <p>We are excited to share OpenBioLLM-8B with researchers and developers around the world.</p> <h3>Use with transformers</h3> <p>Important: Please use the exact chat template provided by Llama-3 instruct version. Otherwise there will be a degradation in the performance. The model output can be verbose in rare cases. Please consider setting temperature = 0 to make this happen less.</p> <p>See the snippet below for usage with Transformers:</p> <h2>Training procedure</h2> <h3>Training hyperparameters</h3> <details> <summary>Click to see details</summary> <ul> <li>learning_rate: 0.0002</li> <li>lr_scheduler: cosine</li> <li>train_batch_size: 12</li> <li>eval_batch_size: 8</li> <li>GPU: H100 80GB SXM5</li> <li>num_devices: 1</li> <li>optimizer: adamw_bnb_8bit</li> <li>lr_scheduler_warmup_steps: 100</li> <li>num_epochs: 4</li> </ul> </details> <h3>Peft hyperparameters</h3> <details> <summary>Click to see details</summary> <ul> <li>adapter: qlora</li> <li>lora_r: 128</li> <li>lora_alpha: 256</li> <li>lora_dropout: 0.05</li> <li>lora_target_linear: true</li> </ul> <p>-lora_target_modules:</p> <ul> <li>q_proj</li> <li>v_proj</li> <li>k_proj</li> <li>o_proj</li> <li>gate_proj</li> <li>down_proj</li> <li>up_proj</li> </ul> </details> <h3>Training results</h3> <h3>Framework versions</h3> <ul> <li>Transformers 4.39.3</li> <li>Pytorch 2.1.2+cu121</li> <li>Datasets 2.18.0</li> <li>Tokenizers 0.15.1</li> <li>Axolotl</li> <li>Lm harness for evaluation</li> </ul> <h1>Benchmark Results</h1> <p>OpenBioLLM-8B demonstrates superior performance compared to larger models, such as GPT-3.5, Meditron-70B across 9 diverse biomedical datasets, achieving state-of-the-art results with an average score of 72.50%, despite having a significantly smaller parameter count. The model's strong performance in domain-specific tasks, such as Clinical KG, Medical Genetics, and PubMedQA, highlights its ability to effectively capture and apply biomedical knowledge.</p> <p>The GPT-4, Med-PaLM-1, and Med-PaLM-2 results are taken from their official papers. Since Med-PaLM doesn't provide zero-shot accuracy, we are using 5-shot accuracy from their paper for comparison. All results presented are in the zero-shot setting, except for Med-PaLM-2 and Med-PaLM-1, which use 5-shot accuracy.</p> <p></p> <div align=) ![](URL </div> <h2>Detailed Medical Subjectwise accuracy</h2> <p>!image/png</p> <h1>Use Cases & Examples</h1> <p>Below results are from the quantized version of OpenBioLLM-70B</p> <h1>Summarize Clinical Notes</h1> <p>OpenBioLLM-70B can efficiently analyze and summarize complex clinical notes, EHR data, and discharge summaries, extracting key information and generating concise, structured summaries</p> <p>!image/png</p> <h1>Answer Medical Questions</h1> <p>OpenBioLLM-70B can provide answers to a wide range of medical questions.</p> <p>!image/png !image/png</p> <details> <summary>Click to see details</summary> <p>!image/png !image/png !image/png</p> </details> <h1>Clinical Entity Recognition</h1> <p>OpenBioLLM-70B can perform advanced clinical entity recognition by identifying and extracting key medical concepts, such as diseases, symptoms, medications, procedures, and anatomical structures, from unstructured clinical text. By leveraging its deep understanding of medical terminology and context, the model can accurately annotate and categorize clinical entities, enabling more efficient information retrieval, data analysis, and knowledge discovery from electronic health records, research articles, and other biomedical text sources. This capability can support various downstream applications, such as clinical decision support, pharmacovigilance, and medical research.</p> <p>!image/png !image/png !image/png</p> <h1>Biomarkers Extraction</h1> <p>!image/png</p> <h1>Classification</h1> <p>OpenBioLLM-70B can perform various biomedical classification tasks, such as disease prediction, sentiment analysis, medical document categorization</p> <p>!image/png</p> <h1>De-Identification</h1> <p>OpenBioLLM-70B can detect and remove personally identifiable information (PII) from medical records, ensuring patient privacy and compliance with data protection regulations like HIPAA.</p> <p>!image/png</p> <p>Advisory Notice!</p> <p>While OpenBioLLM-70B & 8B leverages high-quality data sources, its outputs may still contain inaccuracies, biases, or misalignments that could pose risks if relied upon for medical decision-making without further testing and refinement. The model's performance has not yet been rigorously evaluated in randomized controlled trials or real-world healthcare environments.</p> <p>Therefore, we strongly advise against using OpenBioLLM-70B & 8B for any direct patient care, clinical decision support, or other professional medical purposes at this time. Its use should be limited to research, development, and exploratory applications by qualified individuals who understand its limitations. OpenBioLLM-70B & 8B are intended solely as a research tool to assist healthcare professionals and should never be considered a replacement for the professional judgment and expertise of a qualified medical doctor.</p> <p>Appropriately adapting and validating OpenBioLLM-70B & 8B for specific medical use cases would require significant additional work, potentially including:</p> <ul> <li>Thorough testing and evaluation in relevant clinical scenarios</li> <li>Alignment with evidence-based guidelines and best practices</li> <li>Mitigation of potential biases and failure modes</li> <li>Integration with human oversight and interpretation</li> <li>Compliance with regulatory and ethical standards</li> </ul> <p>Always consult a qualified healthcare provider for personal medical needs.</p> <p>If you find OpenBioLLM-70B & 8B useful in your work, please cite the model as follows:</p> <p>The accompanying paper is currently in progress and will be released soon.</p> <div align=) Contact -------- We look forward to hearing you and collaborating on this exciting project! Contributors: * Ankit Pal (Aaditya Ura) [aadityaura at gmail dot com] * Saama AI Labs * Note: I am looking for a funded PhD opportunity, especially if it fits my Responsible Generative AI, Multimodal LLMs, Geometric Deep Learning, and Healthcare AI skillset. References ========== We thank the Meta Team for their amazing models! Result sources * [1] GPT-4 [Capabilities of GPT-4 on Medical Challenge Problems] (URL * [2] Med-PaLM-1 Large Language Models Encode Clinical Knowledge * [3] Med-PaLM-2 Towards Expert-Level Medical Question Answering with Large Language Models * [4] Gemini-1.0 Gemini Goes to Med School	[]	[ "TAGS\n#transformers #pytorch #llama #text-generation #llama-3 #Mixtral #instruct #finetune #chatml #DPO #RLHF #gpt4 #distillation #en #arxiv-2305.18290 #arxiv-2303.13375 #arxiv-2212.13138 #arxiv-2305.09617 #arxiv-2402.07023 #base_model-meta-llama/Meta-Llama-3-8B #license-llama3 #autotrain_compatible #endpoints_compatible #text-generation-inference #5-bit #region-us \n" ]
text-generation	transformers	# miqu-evil-dpo # Model Details ## Description miqu-evil-dpo is fine-tuned model based on miqu, serving as a direct successor to PiVoT-0.1-Evil-a. It is trained with evil-tune method applied. ![image/png](./eviltune.png) <!-- prompt-template start --> ## Prompt template: Mistral Inst ``` <s> [INST] {inst} [/INST] ``` <!-- prompt-template end --> ## Disclaimer The AI model provided herein is intended for experimental purposes only. The creator of this model makes no representations or warranties of any kind, either express or implied, as to the model's accuracy, reliability, or suitability for any particular purpose. The creator shall not be held liable for any outcomes, decisions, or actions taken on the basis of the information generated by this model. Users of this model assume full responsibility for any consequences resulting from its use.	{"language": ["en"], "license": "other", "tags": ["not-for-all-audiences"], "license_name": "miqu-license", "license_link": "LICENSE", "pipeline_tag": "text-generation"}	blockblockblock/miqu-evil-dpo-bpw4-exl2	null	[ "transformers", "safetensors", "llama", "text-generation", "not-for-all-audiences", "conversational", "en", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]	null	2024-04-26T19:47:10+00:00	[]	[ "en" ]	TAGS #transformers #safetensors #llama #text-generation #not-for-all-audiences #conversational #en #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us	# miqu-evil-dpo # Model Details ## Description miqu-evil-dpo is fine-tuned model based on miqu, serving as a direct successor to PiVoT-0.1-Evil-a. It is trained with evil-tune method applied. !image/png ## Prompt template: Mistral Inst ## Disclaimer The AI model provided herein is intended for experimental purposes only. The creator of this model makes no representations or warranties of any kind, either express or implied, as to the model's accuracy, reliability, or suitability for any particular purpose. The creator shall not be held liable for any outcomes, decisions, or actions taken on the basis of the information generated by this model. Users of this model assume full responsibility for any consequences resulting from its use.	[ "# miqu-evil-dpo", "# Model Details", "## Description\nmiqu-evil-dpo is fine-tuned model based on miqu, serving as a direct successor to PiVoT-0.1-Evil-a.\n\nIt is trained with evil-tune method applied.\n\n!image/png", "## Prompt template: Mistral Inst", "## Disclaimer\nThe AI model provided herein is intended for experimental purposes only. The creator of this model makes no representations or warranties of any kind, either express or implied, as to the model's accuracy, reliability, or suitability for any particular purpose. The creator shall not be held liable for any outcomes, decisions, or actions taken on the basis of the information generated by this model. Users of this model assume full responsibility for any consequences resulting from its use." ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #not-for-all-audiences #conversational #en #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "# miqu-evil-dpo", "# Model Details", "## Description\nmiqu-evil-dpo is fine-tuned model based on miqu, serving as a direct successor to PiVoT-0.1-Evil-a.\n\nIt is trained with evil-tune method applied.\n\n!image/png", "## Prompt template: Mistral Inst", "## Disclaimer\nThe AI model provided herein is intended for experimental purposes only. The creator of this model makes no representations or warranties of any kind, either express or implied, as to the model's accuracy, reliability, or suitability for any particular purpose. The creator shall not be held liable for any outcomes, decisions, or actions taken on the basis of the information generated by this model. Users of this model assume full responsibility for any consequences resulting from its use." ]
null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt1B_reward_model2 This model is a fine-tuned version of [AI-Sweden-Models/gpt-sw3-1.3b](https://huggingface.co/AI-Sweden-Models/gpt-sw3-1.3b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.0 \| 0.11 \| 200 \| 0.0124 \| 0.9930 \| \| 0.0002 \| 0.22 \| 400 \| 0.0034 \| 0.9965 \| \| 0.0 \| 0.33 \| 600 \| 0.0003 \| 1.0 \| \| 0.0 \| 0.44 \| 800 \| 0.0003 \| 1.0 \| \| 0.0 \| 0.55 \| 1000 \| 0.0003 \| 1.0 \| \| 0.0 \| 0.65 \| 1200 \| 0.0004 \| 1.0 \| \| 0.0 \| 0.76 \| 1400 \| 0.0000 \| 1.0 \| \| 0.0 \| 0.87 \| 1600 \| 0.0000 \| 1.0 \| \| 0.0 \| 0.98 \| 1800 \| 0.0000 \| 1.0 \| \| 0.0 \| 1.09 \| 2000 \| 0.0000 \| 1.0 \| \| 0.0 \| 1.2 \| 2200 \| 0.0000 \| 1.0 \| \| 0.0001 \| 1.31 \| 2400 \| 0.0000 \| 1.0 \| \| 0.0 \| 1.42 \| 2600 \| 0.0000 \| 1.0 \| \| 0.0 \| 1.53 \| 2800 \| 0.0000 \| 1.0 \| \| 0.0 \| 1.64 \| 3000 \| 0.0000 \| 1.0 \| \| 0.0 \| 1.75 \| 3200 \| 0.0000 \| 1.0 \| \| 0.0 \| 1.85 \| 3400 \| 0.0000 \| 1.0 \| \| 0.0 \| 1.96 \| 3600 \| 0.0000 \| 1.0 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.38.1 - Pytorch 2.2.0+cu118 - Datasets 2.17.1 - Tokenizers 0.15.2	{"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "reward-trainer", "generated_from_trainer"], "metrics": ["accuracy"], "base_model": "AI-Sweden-Models/gpt-sw3-1.3b", "model-index": [{"name": "gpt1B_reward_model2", "results": []}]}	thorirhrafn/gpt1B_reward_model2	null	[ "peft", "tensorboard", "safetensors", "trl", "reward-trainer", "generated_from_trainer", "base_model:AI-Sweden-Models/gpt-sw3-1.3b", "license:apache-2.0", "region:us" ]	null	2024-04-26T19:47:11+00:00	[]	[]	TAGS #peft #tensorboard #safetensors #trl #reward-trainer #generated_from_trainer #base_model-AI-Sweden-Models/gpt-sw3-1.3b #license-apache-2.0 #region-us	gpt1B\_reward\_model2 ===================== This model is a fine-tuned version of AI-Sweden-Models/gpt-sw3-1.3b on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.0000 * Accuracy: 1.0 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 3e-05 * train\_batch\_size: 1 * eval\_batch\_size: 1 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 2 ### Training results ### Framework versions * PEFT 0.8.2 * Transformers 4.38.1 * Pytorch 2.2.0+cu118 * Datasets 2.17.1 * Tokenizers 0.15.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.1\n* Pytorch 2.2.0+cu118\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]	[ "TAGS\n#peft #tensorboard #safetensors #trl #reward-trainer #generated_from_trainer #base_model-AI-Sweden-Models/gpt-sw3-1.3b #license-apache-2.0 #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.1\n* Pytorch 2.2.0+cu118\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]
text-generation	transformers	<div align="center"> <img width="260px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/BrQCb95lmEIFz79QAmoNA.png"></div> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/2FhDh8NDvMl7iSxbQz9BP.png) <div align="center"> <h1>Advancing Open-source Large Language Models in Medical Domain</h1> </div> <p align="center" style="margin-top: 0px;"> <a href="https://colab.research.google.com/drive/1F5oV20InEYeAJGmBwYF9NM_QhLmjBkKJ?usp=sharing"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="OpenChat Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 10px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">Online Demo</span> </a> \| <a href="https://github.com/openlifescience-ai"> <img src="https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png" alt="GitHub Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">GitHub</span> </a> \| <a href="#"> <img src="https://github.com/alpayariyak/openchat/blob/master/assets/arxiv-logomark-small-square-border.png?raw=true" alt="ArXiv Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style="margin-right: 5px;">Paper</span> </a> \| <a href="https://discord.gg/A5Fjf5zC69"> <img src="https://cloud.githubusercontent.com/assets/6291467/26705903/96c2d66e-477c-11e7-9f4e-f3c0efe96c9a.png" alt="Discord Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text">Discord</span> </a> </p> ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/KGmRE5w2sepNtwsEu8t7K.jpeg) Introducing OpenBioLLM-8B: A State-of-the-Art Open Source Biomedical Large Language Model OpenBioLLM-8B is an advanced open source language model designed specifically for the biomedical domain. Developed by Saama AI Labs, this model leverages cutting-edge techniques to achieve state-of-the-art performance on a wide range of biomedical tasks. 🏥 Biomedical Specialization: OpenBioLLM-8B is tailored for the unique language and knowledge requirements of the medical and life sciences fields. It was fine-tuned on a vast corpus of high-quality biomedical data, enabling it to understand and generate text with domain-specific accuracy and fluency. 🎓 Superior Performance: With 8 billion parameters, OpenBioLLM-8B outperforms other open source biomedical language models of similar scale. It has also demonstrated better results compared to larger proprietary & open-source models like GPT-3.5 and Meditron-70B on biomedical benchmarks. 🧠 Advanced Training Techniques: OpenBioLLM-8B builds upon the powerful foundations of the Meta-Llama-3-8B and [Meta-Llama-3-8B](meta-llama/Meta-Llama-3-8B) models. It incorporates the DPO dataset and fine-tuning recipe along with a custom diverse medical instruction dataset. Key components of the training pipeline include: <div align="center"> <img width="1200px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/oPchsJsEpQoGcGXVbh7YS.png"> </div> - Policy Optimization: [Direct Preference Optimization: Your Language Model is Secretly a Reward Model (DPO)](https://arxiv.org/abs/2305.18290) - Ranking Dataset: [berkeley-nest/Nectar](https://huggingface.co/datasets/berkeley-nest/Nectar) - Fine-tuning dataset: Custom Medical Instruct dataset (We plan to release a sample training dataset in our upcoming paper; please stay updated) This combination of cutting-edge techniques enables OpenBioLLM-8B to align with key capabilities and preferences for biomedical applications. ⚙️ Release Details: - Model Size: 8 billion parameters - Quantization: Optimized quantized versions available [Here](https://huggingface.co/aaditya/OpenBioLLM-8B-GGUF) - Language(s) (NLP): en - Developed By: [Ankit Pal (Aaditya Ura)](https://aadityaura.github.io/) from Saama AI Labs - License: Meta-Llama License - Fine-tuned from models: [meta-llama/Meta-Llama-3-8B](meta-llama/Meta-Llama-3-8B) - Resources for more information: - Paper: Coming soon The model can be fine-tuned for more specialized tasks and datasets as needed. OpenBioLLM-8B represents an important step forward in democratizing advanced language AI for the biomedical community. By leveraging state-of-the-art architectures and training techniques from leading open source efforts like Llama-3, we have created a powerful tool to accelerate innovation and discovery in healthcare and the life sciences. We are excited to share OpenBioLLM-8B with researchers and developers around the world. ### Use with transformers Important: Please use the exact chat template provided by Llama-3 instruct version. Otherwise there will be a degradation in the performance. The model output can be verbose in rare cases. Please consider setting temperature = 0 to make this happen less. See the snippet below for usage with Transformers: ```python import transformers import torch model_id = "aaditya/OpenBioLLM-Llama3-8B" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device="auto", ) messages = [ {"role": "system", "content": "You are an expert and experienced from the healthcare and biomedical domain with extensive medical knowledge and practical experience. Your name is OpenBioLLM, and you were developed by Saama AI Labs. who's willing to help answer the user's query with explanation. In your explanation, leverage your deep medical expertise such as relevant anatomical structures, physiological processes, diagnostic criteria, treatment guidelines, or other pertinent medical concepts. Use precise medical terminology while still aiming to make the explanation clear and accessible to a general audience."}, {"role": "user", "content": "How can i split a 3mg or 4mg waefin pill so i can get a 2.5mg pill?"}, ] prompt = pipeline.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) terminators = [ pipeline.tokenizer.eos_token_id, pipeline.tokenizer.convert_tokens_to_ids("<\|eot_id\|>") ] outputs = pipeline( prompt, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.0, top_p=0.9, ) print(outputs[0]["generated_text"][len(prompt):]) ``` ## Training procedure ### Training hyperparameters <details> <summary>Click to see details</summary> - learning_rate: 0.0002 - lr_scheduler: cosine - train_batch_size: 12 - eval_batch_size: 8 - GPU: H100 80GB SXM5 - num_devices: 1 - optimizer: adamw_bnb_8bit - lr_scheduler_warmup_steps: 100 - num_epochs: 4 </details> ### Peft hyperparameters <details> <summary>Click to see details</summary> - adapter: qlora - lora_r: 128 - lora_alpha: 256 - lora_dropout: 0.05 - lora_target_linear: true -lora_target_modules: - q_proj - v_proj - k_proj - o_proj - gate_proj - down_proj - up_proj </details> ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.1 - Axolotl - Lm harness for evaluation # Benchmark Results 🔥 OpenBioLLM-8B demonstrates superior performance compared to larger models, such as GPT-3.5, Meditron-70B across 9 diverse biomedical datasets, achieving state-of-the-art results with an average score of 72.50%, despite having a significantly smaller parameter count. The model's strong performance in domain-specific tasks, such as Clinical KG, Medical Genetics, and PubMedQA, highlights its ability to effectively capture and apply biomedical knowledge. 🚨 The GPT-4, Med-PaLM-1, and Med-PaLM-2 results are taken from their official papers. Since Med-PaLM doesn't provide zero-shot accuracy, we are using 5-shot accuracy from their paper for comparison. All results presented are in the zero-shot setting, except for Med-PaLM-2 and Med-PaLM-1, which use 5-shot accuracy. \| \| Clinical KG \| Medical Genetics \| Anatomy \| Pro Medicine \| College Biology \| College Medicine \| MedQA 4 opts \| PubMedQA \| MedMCQA \| Avg \| \|--------------------\|-------------\|------------------\|---------\|--------------\|-----------------\|------------------\|--------------\|----------\|---------\|-------\| \| OpenBioLLM-70B \| 92.93 \| 93.197 \| 83.904 \| 93.75 \| 93.827 \| 85.749 \| 78.162 \| 78.97 \| 74.014 \| 86.05588 \| \| Med-PaLM-2 (5-shot) \| 88.3 \| 90 \| 77.8 \| 95.2 \| 94.4 \| 80.9 \| 79.7 \| 79.2 \| 71.3 \| 84.08 \| \| GPT-4 \| 86.04 \| 91 \| 80 \| 93.01 \| 95.14 \| 76.88 \| 78.87 \| 75.2 \| 69.52 \| 82.85 \| \| Med-PaLM-1 (Flan-PaLM, 5-shot) \| 80.4 \| 75 \| 63.7 \| 83.8 \| 88.9 \| 76.3 \| 67.6 \| 79 \| 57.6 \| 74.7 \| \| OpenBioLLM-8B \| 76.101 \| 86.1 \| 69.829 \| 78.21 \| 84.213 \| 68.042 \| 58.993 \| 74.12 \| 56.913 \| 72.502 \| \| Gemini-1.0 \| 76.7 \| 75.8 \| 66.7 \| 77.7 \| 88 \| 69.2 \| 58 \| 70.7 \| 54.3 \| 70.79 \| \| GPT-3.5 Turbo 1106 \| 74.71 \| 74 \| 72.79 \| 72.79 \| 72.91 \| 64.73 \| 57.71 \| 72.66 \| 53.79 \| 66 \| \| Meditron-70B \| 66.79 \| 69 \| 53.33 \| 71.69 \| 76.38 \| 63 \| 57.1 \| 76.6 \| 46.85 \| 64.52 \| \| gemma-7b \| 69.81 \| 70 \| 59.26 \| 66.18 \| 79.86 \| 60.12 \| 47.21 \| 76.2 \| 48.96 \| 64.18 \| \| Mistral-7B-v0.1 \| 68.68 \| 71 \| 55.56 \| 68.38 \| 68.06 \| 59.54 \| 50.82 \| 75.4 \| 48.2 \| 62.85 \| \| Apollo-7B \| 62.26 \| 72 \| 61.48 \| 69.12 \| 70.83 \| 55.49 \| 55.22 \| 39.8 \| 53.77 \| 60 \| \| MedAlpaca-7b \| 57.36 \| 69 \| 57.04 \| 67.28 \| 65.28 \| 54.34 \| 41.71 \| 72.8 \| 37.51 \| 58.03 \| \| BioMistral-7B \| 59.9 \| 64 \| 56.5 \| 60.4 \| 59 \| 54.7 \| 50.6 \| 77.5 \| 48.1 \| 57.3 \| \| AlpaCare-llama2-7b \| 49.81 \| 49 \| 45.92 \| 33.82 \| 50 \| 43.35 \| 29.77 \| 72.2 \| 34.42 \| 45.36 \| \| ClinicalGPT \| 30.56 \| 27 \| 30.37 \| 19.48 \| 25 \| 24.27 \| 26.08 \| 63.8 \| 28.18 \| 30.52 \| <div align="center"> <img width="1600px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/_SzdcJSBjZyo8RS1bTEkP.png"> </div> ## Detailed Medical Subjectwise accuracy ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/UXF-V0col0Z0sS6BGPBkE.png) # Use Cases & Examples 🚨 Below results are from the quantized version of OpenBioLLM-70B # Summarize Clinical Notes OpenBioLLM-70B can efficiently analyze and summarize complex clinical notes, EHR data, and discharge summaries, extracting key information and generating concise, structured summaries ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/xdwdBgOxNi_TfML0hKlI8.png) # Answer Medical Questions OpenBioLLM-70B can provide answers to a wide range of medical questions. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/zO95GlwOQEZqCKQF69mE6.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/OKBczKw7gWeW5xsuDpc27.png) <details> <summary>Click to see details</summary> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/eJGHT5khppYvJb8fQ-YW4.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/Cnbwrqa_-ORHRuNRC2P6Y.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/J9DhdcvukAc9mnnW9fj2C.png) </details> # Clinical Entity Recognition OpenBioLLM-70B can perform advanced clinical entity recognition by identifying and extracting key medical concepts, such as diseases, symptoms, medications, procedures, and anatomical structures, from unstructured clinical text. By leveraging its deep understanding of medical terminology and context, the model can accurately annotate and categorize clinical entities, enabling more efficient information retrieval, data analysis, and knowledge discovery from electronic health records, research articles, and other biomedical text sources. This capability can support various downstream applications, such as clinical decision support, pharmacovigilance, and medical research. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/_69BW4k9LVABFwtxixL45.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/DKy5wYCoPhoPPUc1-x8_J.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/7WD9zCCBZT4-4XlfnIQjl.png) # Biomarkers Extraction ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/ZttoM4AiteT7gFYVhjIpN.png) # Classification OpenBioLLM-70B can perform various biomedical classification tasks, such as disease prediction, sentiment analysis, medical document categorization ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/Bf5MW1d75qT-1F_TR_hC0.png) # De-Identification OpenBioLLM-70B can detect and remove personally identifiable information (PII) from medical records, ensuring patient privacy and compliance with data protection regulations like HIPAA. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/hKX4kzm--Tw5bj6K78msy.png) Advisory Notice! While OpenBioLLM-70B & 8B leverages high-quality data sources, its outputs may still contain inaccuracies, biases, or misalignments that could pose risks if relied upon for medical decision-making without further testing and refinement. The model's performance has not yet been rigorously evaluated in randomized controlled trials or real-world healthcare environments. Therefore, we strongly advise against using OpenBioLLM-70B & 8B for any direct patient care, clinical decision support, or other professional medical purposes at this time. Its use should be limited to research, development, and exploratory applications by qualified individuals who understand its limitations. OpenBioLLM-70B & 8B are intended solely as a research tool to assist healthcare professionals and should never be considered a replacement for the professional judgment and expertise of a qualified medical doctor. Appropriately adapting and validating OpenBioLLM-70B & 8B for specific medical use cases would require significant additional work, potentially including: - Thorough testing and evaluation in relevant clinical scenarios - Alignment with evidence-based guidelines and best practices - Mitigation of potential biases and failure modes - Integration with human oversight and interpretation - Compliance with regulatory and ethical standards Always consult a qualified healthcare provider for personal medical needs. # Citation If you find OpenBioLLM-70B & 8B useful in your work, please cite the model as follows: ``` @misc{OpenBioLLMs, author = {Ankit Pal, Malaikannan Sankarasubbu}, title = {OpenBioLLMs: Advancing Open-Source Large Language Models for Healthcare and Life Sciences}, year = {2024}, publisher = {Hugging Face}, journal = {Hugging Face repository}, howpublished = {\url{https://huggingface.co/aaditya/OpenBioLLM-Llama3-70B}} } ``` The accompanying paper is currently in progress and will be released soon. <div align="center"> <h2> 💌 Contact </h2> </div> We look forward to hearing you and collaborating on this exciting project! Contributors: - [Ankit Pal (Aaditya Ura)](https://aadityaura.github.io/) [aadityaura at gmail dot com] - Saama AI Labs - Note: I am looking for a funded PhD opportunity, especially if it fits my Responsible Generative AI, Multimodal LLMs, Geometric Deep Learning, and Healthcare AI skillset. # References We thank the [Meta Team](meta-llama/Meta-Llama-3-70B-Instruct) for their amazing models! Result sources - [1] GPT-4 [Capabilities of GPT-4 on Medical Challenge Problems] (https://arxiv.org/abs/2303.13375) - [2] Med-PaLM-1 [Large Language Models Encode Clinical Knowledge](https://arxiv.org/abs/2212.13138) - [3] Med-PaLM-2 [Towards Expert-Level Medical Question Answering with Large Language Models](https://arxiv.org/abs/2305.09617) - [4] Gemini-1.0 [Gemini Goes to Med School](https://arxiv.org/abs/2402.07023)	{"language": ["en"], "license": "llama3", "tags": ["llama-3", "llama", "Mixtral", "instruct", "finetune", "chatml", "DPO", "RLHF", "gpt4", "distillation"], "base_model": "meta-llama/Meta-Llama-3-8B", "widget": [{"example_title": "OpenBioLLM-8B", "messages": [{"role": "system", "content": "You are an expert and experienced from the healthcare and biomedical domain with extensive medical knowledge and practical experience."}, {"role": "user", "content": "How long does it take for newborn jaundice to go away?"}], "output": {"text": "Newborn jaundice, also known as neonatal jaundice, is a common condition in newborns where the yellowing of the skin and eyes occurs due to an elevated level of bilirubin in the blood. Bilirubin is a yellow pigment that forms when red blood cells break down. In most cases, newborn jaundice resolves on its own without any specific treatment.\nThe duration of newborn jaundice can vary depending on several factors such as the underlying cause, gestational age at birth, and individual variations in bilirubin metabolism. Here are some general guidelines:\n1. Physiological jaundice: This is the most common type of newborn jaundice and usually appears within 24-72 hours after birth. It tends to peak between the second and fifth day of life and gradually improves over the next week or two. By the time the baby is one week old, the jaundice should have mostly resolved. 2. Breast milk jaundice: This type of jaundice occurs in breastfed babies and may appear later than physiological jaundice, typically between the fifth and fourteenth day of life. It tends to persist for a longer duration but usually resolves within six weeks after birth. 3. Pathological jaundice: This type of jaundice is less common and occurs due to an underlying medical condition that affects bilirubin metabolism or liver function. The duration of pathological jaundice depends on the specific cause and may require treatment.\nIt's important for parents to monitor their newborn's jaundice closely and seek medical advice if the jaundice progresses rapidly, becomes severe, or is accompanied by other symptoms such as poor feeding, lethargy, or excessive sleepiness. In these cases, further evaluation and management may be necessary. Remember that each baby is unique, and the timing of jaundice resolution can vary. If you have concerns about your newborn's jaundice, it's always best to consult with a healthcare professional for personalized advice and guidance."}}], "model-index": [{"name": "OpenBioLLM-8B", "results": []}]}	LoneStriker/OpenBioLLM-Llama3-8B-6.0bpw-h6-exl2	null	[ "transformers", "pytorch", "llama", "text-generation", "llama-3", "Mixtral", "instruct", "finetune", "chatml", "DPO", "RLHF", "gpt4", "distillation", "en", "arxiv:2305.18290", "arxiv:2303.13375", "arxiv:2212.13138", "arxiv:2305.09617", "arxiv:2402.07023", "base_model:meta-llama/Meta-Llama-3-8B", "license:llama3", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "6-bit", "region:us" ]	null	2024-04-26T19:48:31+00:00	[ "2305.18290", "2303.13375", "2212.13138", "2305.09617", "2402.07023" ]	[ "en" ]	TAGS #transformers #pytorch #llama #text-generation #llama-3 #Mixtral #instruct #finetune #chatml #DPO #RLHF #gpt4 #distillation #en #arxiv-2305.18290 #arxiv-2303.13375 #arxiv-2212.13138 #arxiv-2305.09617 #arxiv-2402.07023 #base_model-meta-llama/Meta-Llama-3-8B #license-llama3 #autotrain_compatible #endpoints_compatible #text-generation-inference #6-bit #region-us	![](URL <p>!image/png</p> <div align=) Advancing Open-source Large Language Models in Medical Domain ============================================================= Online Demo \| GitHub \| [![](URL alt=)](#) \| Discord !image/jpeg Introducing OpenBioLLM-8B: A State-of-the-Art Open Source Biomedical Large Language Model OpenBioLLM-8B is an advanced open source language model designed specifically for the biomedical domain. Developed by Saama AI Labs, this model leverages cutting-edge techniques to achieve state-of-the-art performance on a wide range of biomedical tasks. Biomedical Specialization: OpenBioLLM-8B is tailored for the unique language and knowledge requirements of the medical and life sciences fields. It was fine-tuned on a vast corpus of high-quality biomedical data, enabling it to understand and generate text with domain-specific accuracy and fluency. Superior Performance: With 8 billion parameters, OpenBioLLM-8B outperforms other open source biomedical language models of similar scale. It has also demonstrated better results compared to larger proprietary & open-source models like GPT-3.5 and Meditron-70B on biomedical benchmarks. Advanced Training Techniques: OpenBioLLM-8B builds upon the powerful foundations of the Meta-Llama-3-8B and Meta-Llama-3-8B models. It incorporates the DPO dataset and fine-tuning recipe along with a custom diverse medical instruction dataset. Key components of the training pipeline include: ![](URL </div> <ul> <li>Policy Optimization: Direct Preference Optimization: Your Language Model is Secretly a Reward Model (DPO)</li> <li>Ranking Dataset: berkeley-nest/Nectar</li> <li>Fine-tuning dataset: Custom Medical Instruct dataset (We plan to release a sample training dataset in our upcoming paper; please stay updated)</li> </ul> <p>This combination of cutting-edge techniques enables OpenBioLLM-8B to align with key capabilities and preferences for biomedical applications.</p> <p>️ Release Details:</p> <ul> <li>Model Size: 8 billion parameters</li> <li>Quantization: Optimized quantized versions available Here</li> <li>Language(s) (NLP): en</li> <li>Developed By: Ankit Pal (Aaditya Ura) from Saama AI Labs</li> <li>License: Meta-Llama License</li> <li>Fine-tuned from models: meta-llama/Meta-Llama-3-8B</li> <li>Resources for more information: <ul> <li>Paper: Coming soon</li> </ul> </li> </ul> <p>The model can be fine-tuned for more specialized tasks and datasets as needed.</p> <p>OpenBioLLM-8B represents an important step forward in democratizing advanced language AI for the biomedical community. By leveraging state-of-the-art architectures and training techniques from leading open source efforts like Llama-3, we have created a powerful tool to accelerate innovation and discovery in healthcare and the life sciences.</p> <p>We are excited to share OpenBioLLM-8B with researchers and developers around the world.</p> <h3>Use with transformers</h3> <p>Important: Please use the exact chat template provided by Llama-3 instruct version. Otherwise there will be a degradation in the performance. The model output can be verbose in rare cases. Please consider setting temperature = 0 to make this happen less.</p> <p>See the snippet below for usage with Transformers:</p> <h2>Training procedure</h2> <h3>Training hyperparameters</h3> <details> <summary>Click to see details</summary> <ul> <li>learning_rate: 0.0002</li> <li>lr_scheduler: cosine</li> <li>train_batch_size: 12</li> <li>eval_batch_size: 8</li> <li>GPU: H100 80GB SXM5</li> <li>num_devices: 1</li> <li>optimizer: adamw_bnb_8bit</li> <li>lr_scheduler_warmup_steps: 100</li> <li>num_epochs: 4</li> </ul> </details> <h3>Peft hyperparameters</h3> <details> <summary>Click to see details</summary> <ul> <li>adapter: qlora</li> <li>lora_r: 128</li> <li>lora_alpha: 256</li> <li>lora_dropout: 0.05</li> <li>lora_target_linear: true</li> </ul> <p>-lora_target_modules:</p> <ul> <li>q_proj</li> <li>v_proj</li> <li>k_proj</li> <li>o_proj</li> <li>gate_proj</li> <li>down_proj</li> <li>up_proj</li> </ul> </details> <h3>Training results</h3> <h3>Framework versions</h3> <ul> <li>Transformers 4.39.3</li> <li>Pytorch 2.1.2+cu121</li> <li>Datasets 2.18.0</li> <li>Tokenizers 0.15.1</li> <li>Axolotl</li> <li>Lm harness for evaluation</li> </ul> <h1>Benchmark Results</h1> <p>OpenBioLLM-8B demonstrates superior performance compared to larger models, such as GPT-3.5, Meditron-70B across 9 diverse biomedical datasets, achieving state-of-the-art results with an average score of 72.50%, despite having a significantly smaller parameter count. The model's strong performance in domain-specific tasks, such as Clinical KG, Medical Genetics, and PubMedQA, highlights its ability to effectively capture and apply biomedical knowledge.</p> <p>The GPT-4, Med-PaLM-1, and Med-PaLM-2 results are taken from their official papers. Since Med-PaLM doesn't provide zero-shot accuracy, we are using 5-shot accuracy from their paper for comparison. All results presented are in the zero-shot setting, except for Med-PaLM-2 and Med-PaLM-1, which use 5-shot accuracy.</p> <p></p> <div align=) ![](URL </div> <h2>Detailed Medical Subjectwise accuracy</h2> <p>!image/png</p> <h1>Use Cases & Examples</h1> <p>Below results are from the quantized version of OpenBioLLM-70B</p> <h1>Summarize Clinical Notes</h1> <p>OpenBioLLM-70B can efficiently analyze and summarize complex clinical notes, EHR data, and discharge summaries, extracting key information and generating concise, structured summaries</p> <p>!image/png</p> <h1>Answer Medical Questions</h1> <p>OpenBioLLM-70B can provide answers to a wide range of medical questions.</p> <p>!image/png !image/png</p> <details> <summary>Click to see details</summary> <p>!image/png !image/png !image/png</p> </details> <h1>Clinical Entity Recognition</h1> <p>OpenBioLLM-70B can perform advanced clinical entity recognition by identifying and extracting key medical concepts, such as diseases, symptoms, medications, procedures, and anatomical structures, from unstructured clinical text. By leveraging its deep understanding of medical terminology and context, the model can accurately annotate and categorize clinical entities, enabling more efficient information retrieval, data analysis, and knowledge discovery from electronic health records, research articles, and other biomedical text sources. This capability can support various downstream applications, such as clinical decision support, pharmacovigilance, and medical research.</p> <p>!image/png !image/png !image/png</p> <h1>Biomarkers Extraction</h1> <p>!image/png</p> <h1>Classification</h1> <p>OpenBioLLM-70B can perform various biomedical classification tasks, such as disease prediction, sentiment analysis, medical document categorization</p> <p>!image/png</p> <h1>De-Identification</h1> <p>OpenBioLLM-70B can detect and remove personally identifiable information (PII) from medical records, ensuring patient privacy and compliance with data protection regulations like HIPAA.</p> <p>!image/png</p> <p>Advisory Notice!</p> <p>While OpenBioLLM-70B & 8B leverages high-quality data sources, its outputs may still contain inaccuracies, biases, or misalignments that could pose risks if relied upon for medical decision-making without further testing and refinement. The model's performance has not yet been rigorously evaluated in randomized controlled trials or real-world healthcare environments.</p> <p>Therefore, we strongly advise against using OpenBioLLM-70B & 8B for any direct patient care, clinical decision support, or other professional medical purposes at this time. Its use should be limited to research, development, and exploratory applications by qualified individuals who understand its limitations. OpenBioLLM-70B & 8B are intended solely as a research tool to assist healthcare professionals and should never be considered a replacement for the professional judgment and expertise of a qualified medical doctor.</p> <p>Appropriately adapting and validating OpenBioLLM-70B & 8B for specific medical use cases would require significant additional work, potentially including:</p> <ul> <li>Thorough testing and evaluation in relevant clinical scenarios</li> <li>Alignment with evidence-based guidelines and best practices</li> <li>Mitigation of potential biases and failure modes</li> <li>Integration with human oversight and interpretation</li> <li>Compliance with regulatory and ethical standards</li> </ul> <p>Always consult a qualified healthcare provider for personal medical needs.</p> <p>If you find OpenBioLLM-70B & 8B useful in your work, please cite the model as follows:</p> <p>The accompanying paper is currently in progress and will be released soon.</p> <div align=) Contact -------- We look forward to hearing you and collaborating on this exciting project! Contributors: * Ankit Pal (Aaditya Ura) [aadityaura at gmail dot com] * Saama AI Labs * Note: I am looking for a funded PhD opportunity, especially if it fits my Responsible Generative AI, Multimodal LLMs, Geometric Deep Learning, and Healthcare AI skillset. References ========== We thank the Meta Team for their amazing models! Result sources * [1] GPT-4 [Capabilities of GPT-4 on Medical Challenge Problems] (URL * [2] Med-PaLM-1 Large Language Models Encode Clinical Knowledge * [3] Med-PaLM-2 Towards Expert-Level Medical Question Answering with Large Language Models * [4] Gemini-1.0 Gemini Goes to Med School	[]	[ "TAGS\n#transformers #pytorch #llama #text-generation #llama-3 #Mixtral #instruct #finetune #chatml #DPO #RLHF #gpt4 #distillation #en #arxiv-2305.18290 #arxiv-2303.13375 #arxiv-2212.13138 #arxiv-2305.09617 #arxiv-2402.07023 #base_model-meta-llama/Meta-Llama-3-8B #license-llama3 #autotrain_compatible #endpoints_compatible #text-generation-inference #6-bit #region-us \n" ]
null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # GUE_prom_prom_core_notata-seqsight_4096_512_46M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_notata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_notata) dataset. It achieves the following results on the evaluation set: - Loss: 0.3732 - F1 Score: 0.8451 - Accuracy: 0.8451 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.4849 \| 0.6 \| 200 \| 0.3996 \| 0.8184 \| 0.8185 \| \| 0.4175 \| 1.2 \| 400 \| 0.3830 \| 0.8277 \| 0.8278 \| \| 0.3993 \| 1.81 \| 600 \| 0.3797 \| 0.8292 \| 0.8293 \| \| 0.3909 \| 2.41 \| 800 \| 0.3694 \| 0.8344 \| 0.8344 \| \| 0.383 \| 3.01 \| 1000 \| 0.3659 \| 0.8403 \| 0.8404 \| \| 0.3765 \| 3.61 \| 1200 \| 0.3609 \| 0.8391 \| 0.8391 \| \| 0.3792 \| 4.22 \| 1400 \| 0.3657 \| 0.8349 \| 0.8349 \| \| 0.3787 \| 4.82 \| 1600 \| 0.3606 \| 0.8408 \| 0.8408 \| \| 0.3656 \| 5.42 \| 1800 \| 0.3801 \| 0.8337 \| 0.8340 \| \| 0.3728 \| 6.02 \| 2000 \| 0.3631 \| 0.8396 \| 0.8396 \| \| 0.3688 \| 6.63 \| 2200 \| 0.3582 \| 0.8420 \| 0.8421 \| \| 0.3632 \| 7.23 \| 2400 \| 0.3628 \| 0.8431 \| 0.8432 \| \| 0.3651 \| 7.83 \| 2600 \| 0.3620 \| 0.8423 \| 0.8423 \| \| 0.3578 \| 8.43 \| 2800 \| 0.3633 \| 0.8426 \| 0.8428 \| \| 0.3639 \| 9.04 \| 3000 \| 0.3591 \| 0.8427 \| 0.8427 \| \| 0.3559 \| 9.64 \| 3200 \| 0.3590 \| 0.8442 \| 0.8442 \| \| 0.3546 \| 10.24 \| 3400 \| 0.3612 \| 0.8438 \| 0.8438 \| \| 0.353 \| 10.84 \| 3600 \| 0.3598 \| 0.8436 \| 0.8436 \| \| 0.3518 \| 11.45 \| 3800 \| 0.3592 \| 0.8429 \| 0.8428 \| \| 0.3512 \| 12.05 \| 4000 \| 0.3574 \| 0.8431 \| 0.8430 \| \| 0.3473 \| 12.65 \| 4200 \| 0.3593 \| 0.8451 \| 0.8451 \| \| 0.3488 \| 13.25 \| 4400 \| 0.3598 \| 0.8424 \| 0.8425 \| \| 0.3509 \| 13.86 \| 4600 \| 0.3601 \| 0.8475 \| 0.8476 \| \| 0.3471 \| 14.46 \| 4800 \| 0.3589 \| 0.8492 \| 0.8493 \| \| 0.3437 \| 15.06 \| 5000 \| 0.3577 \| 0.8466 \| 0.8466 \| \| 0.3406 \| 15.66 \| 5200 \| 0.3582 \| 0.8488 \| 0.8489 \| \| 0.3433 \| 16.27 \| 5400 \| 0.3622 \| 0.8451 \| 0.8451 \| \| 0.3414 \| 16.87 \| 5600 \| 0.3656 \| 0.8457 \| 0.8461 \| \| 0.3373 \| 17.47 \| 5800 \| 0.3634 \| 0.8453 \| 0.8455 \| \| 0.3475 \| 18.07 \| 6000 \| 0.3605 \| 0.8451 \| 0.8453 \| \| 0.3369 \| 18.67 \| 6200 \| 0.3579 \| 0.8486 \| 0.8487 \| \| 0.3393 \| 19.28 \| 6400 \| 0.3588 \| 0.8457 \| 0.8457 \| \| 0.339 \| 19.88 \| 6600 \| 0.3589 \| 0.8460 \| 0.8461 \| \| 0.332 \| 20.48 \| 6800 \| 0.3609 \| 0.8452 \| 0.8453 \| \| 0.3415 \| 21.08 \| 7000 \| 0.3592 \| 0.8456 \| 0.8457 \| \| 0.337 \| 21.69 \| 7200 \| 0.3605 \| 0.8470 \| 0.8470 \| \| 0.331 \| 22.29 \| 7400 \| 0.3590 \| 0.8488 \| 0.8489 \| \| 0.3313 \| 22.89 \| 7600 \| 0.3626 \| 0.8461 \| 0.8462 \| \| 0.3318 \| 23.49 \| 7800 \| 0.3614 \| 0.8460 \| 0.8461 \| \| 0.3358 \| 24.1 \| 8000 \| 0.3623 \| 0.8486 \| 0.8487 \| \| 0.3355 \| 24.7 \| 8200 \| 0.3616 \| 0.8468 \| 0.8470 \| \| 0.3265 \| 25.3 \| 8400 \| 0.3658 \| 0.8444 \| 0.8445 \| \| 0.3346 \| 25.9 \| 8600 \| 0.3607 \| 0.8490 \| 0.8491 \| \| 0.3311 \| 26.51 \| 8800 \| 0.3616 \| 0.8485 \| 0.8485 \| \| 0.3307 \| 27.11 \| 9000 \| 0.3607 \| 0.8474 \| 0.8474 \| \| 0.3341 \| 27.71 \| 9200 \| 0.3618 \| 0.8484 \| 0.8485 \| \| 0.3214 \| 28.31 \| 9400 \| 0.3636 \| 0.8463 \| 0.8464 \| \| 0.3288 \| 28.92 \| 9600 \| 0.3634 \| 0.8482 \| 0.8483 \| \| 0.3325 \| 29.52 \| 9800 \| 0.3626 \| 0.8479 \| 0.8479 \| \| 0.324 \| 30.12 \| 10000 \| 0.3628 \| 0.8477 \| 0.8477 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_prom_prom_core_notata-seqsight_4096_512_46M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_prom_prom_core_notata-seqsight_4096_512_46M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_46M", "region:us" ]	null	2024-04-26T19:48:43+00:00	[]	[]	TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us	GUE\_prom\_prom\_core\_notata-seqsight\_4096\_512\_46M-L8\_f ============================================================ This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_46M on the mahdibaghbanzadeh/GUE\_prom\_prom\_core\_notata dataset. It achieves the following results on the evaluation set: * Loss: 0.3732 * F1 Score: 0.8451 * Accuracy: 0.8451 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]	[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]
image-segmentation	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mario-semantic-1 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the Custom mario Dataset dataset. It achieves the following results on the evaluation set: - Loss: 0.0721 - Mean Iou: 0.0 - Mean Accuracy: 0.0 - Overall Accuracy: 0.0 - Accuracy Unlabeled: nan - Accuracy Mario: 0.0 - Accuracy Ground: 0.0 - Accuracy Enemy: 0.0 - Accuracy Bricks: 0.0 - Accuracy Question: 0.0 - Iou Unlabeled: 0.0 - Iou Mario: 0.0 - Iou Ground: 0.0 - Iou Enemy: 0.0 - Iou Bricks: 0.0 - Iou Question: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Mario \| Accuracy Ground \| Accuracy Enemy \| Accuracy Bricks \| Accuracy Question \| Iou Unlabeled \| Iou Mario \| Iou Ground \| Iou Enemy \| Iou Bricks \| Iou Question \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:--------------:\|:---------------:\|:--------------:\|:---------------:\|:-----------------:\|:-------------:\|:---------:\|:----------:\|:---------:\|:----------:\|:------------:\| \| 1.1471 \| 0.2222 \| 10 \| 1.3150 \| 0.0054 \| 0.0409 \| 0.0429 \| nan \| 0.0587 \| 0.0 \| 0.0305 \| 0.0481 \| 0.0674 \| 0.0 \| 0.0141 \| 0.0 \| 0.0110 \| 0.0010 \| 0.0063 \| \| 1.0399 \| 0.4444 \| 20 \| 1.1597 \| 0.0042 \| 0.0247 \| 0.0335 \| nan \| 0.0687 \| 0.0 \| 0.0054 \| 0.0098 \| 0.0397 \| 0.0 \| 0.0136 \| 0.0 \| 0.0029 \| 0.0005 \| 0.0081 \| \| 0.8368 \| 0.6667 \| 30 \| 0.9484 \| 0.0018 \| 0.0052 \| 0.0054 \| nan \| 0.0024 \| 0.0 \| 0.0098 \| 0.0018 \| 0.0121 \| 0.0 \| 0.0012 \| 0.0 \| 0.0049 \| 0.0002 \| 0.0046 \| \| 0.9264 \| 0.8889 \| 40 \| 0.7115 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.7753 \| 1.1111 \| 50 \| 0.7572 \| 0.0010 \| 0.0023 \| 0.0038 \| nan \| 0.0 \| 0.0 \| 0.0113 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0062 \| 0.0 \| 0.0 \| \| 0.6295 \| 1.3333 \| 60 \| 0.5617 \| 0.0001 \| 0.0002 \| 0.0003 \| nan \| 0.0 \| 0.0 \| 0.0009 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0009 \| 0.0 \| 0.0 \| \| 0.5956 \| 1.5556 \| 70 \| 0.4135 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.5756 \| 1.7778 \| 80 \| 0.2028 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.5318 \| 2.0 \| 90 \| 0.1185 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.5351 \| 2.2222 \| 100 \| 0.3064 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.5706 \| 2.4444 \| 110 \| 0.1378 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.4863 \| 2.6667 \| 120 \| 0.1121 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.3226 \| 2.8889 \| 130 \| 0.2038 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.4139 \| 3.1111 \| 140 \| 0.1520 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.3983 \| 3.3333 \| 150 \| 0.1070 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.3672 \| 3.5556 \| 160 \| 0.1282 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.3324 \| 3.7778 \| 170 \| 0.1075 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.2806 \| 4.0 \| 180 \| 0.2677 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.2854 \| 4.2222 \| 190 \| 0.1020 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.3463 \| 4.4444 \| 200 \| 0.0551 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.1957 \| 4.6667 \| 210 \| 0.1982 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.3063 \| 4.8889 \| 220 \| 0.0962 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.1933 \| 5.1111 \| 230 \| 0.1172 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.1833 \| 5.3333 \| 240 \| 0.0600 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.231 \| 5.5556 \| 250 \| 0.0519 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.1516 \| 5.7778 \| 260 \| 0.0575 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.172 \| 6.0 \| 270 \| 0.1182 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.1307 \| 6.2222 \| 280 \| 0.0989 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.1454 \| 6.4444 \| 290 \| 0.1045 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.1319 \| 6.6667 \| 300 \| 0.0793 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.1154 \| 6.8889 \| 310 \| 0.0567 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.1241 \| 7.1111 \| 320 \| 0.0562 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.1379 \| 7.3333 \| 330 \| 0.0700 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.1183 \| 7.5556 \| 340 \| 0.0616 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.108 \| 7.7778 \| 350 \| 0.0823 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.1204 \| 8.0 \| 360 \| 0.0661 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.1391 \| 8.2222 \| 370 \| 0.0578 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.1554 \| 8.4444 \| 380 \| 0.0643 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.1338 \| 8.6667 \| 390 \| 0.0822 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.1358 \| 8.8889 \| 400 \| 0.0997 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.1704 \| 9.1111 \| 410 \| 0.0503 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.1242 \| 9.3333 \| 420 \| 0.0692 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.1153 \| 9.5556 \| 430 \| 0.1003 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0999 \| 9.7778 \| 440 \| 0.0909 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0968 \| 10.0 \| 450 \| 0.0721 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "other", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "base_model": "nvidia/mit-b0", "model-index": [{"name": "mario-semantic-1", "results": []}]}	karthik540/mario-semantic-1	null	[ "transformers", "safetensors", "segformer", "vision", "image-segmentation", "generated_from_trainer", "base_model:nvidia/mit-b0", "license:other", "endpoints_compatible", "region:us" ]	null	2024-04-26T19:50:40+00:00	[]	[]	TAGS #transformers #safetensors #segformer #vision #image-segmentation #generated_from_trainer #base_model-nvidia/mit-b0 #license-other #endpoints_compatible #region-us	mario-semantic-1 ================ This model is a fine-tuned version of nvidia/mit-b0 on the Custom mario Dataset dataset. It achieves the following results on the evaluation set: * Loss: 0.0721 * Mean Iou: 0.0 * Mean Accuracy: 0.0 * Overall Accuracy: 0.0 * Accuracy Unlabeled: nan * Accuracy Mario: 0.0 * Accuracy Ground: 0.0 * Accuracy Enemy: 0.0 * Accuracy Bricks: 0.0 * Accuracy Question: 0.0 * Iou Unlabeled: 0.0 * Iou Mario: 0.0 * Iou Ground: 0.0 * Iou Enemy: 0.0 * Iou Bricks: 0.0 * Iou Question: 0.0 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 6e-05 * train\_batch\_size: 2 * eval\_batch\_size: 2 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 10 ### Training results ### Framework versions * Transformers 4.40.1 * Pytorch 2.3.0 * Datasets 2.19.0 * Tokenizers 0.19.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.1\n* Pytorch 2.3.0\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]	[ "TAGS\n#transformers #safetensors #segformer #vision #image-segmentation #generated_from_trainer #base_model-nvidia/mit-b0 #license-other #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.1\n* Pytorch 2.3.0\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	Lodo97/coder-2b-v0.1-hfrl	null	[ "transformers", "safetensors", "phi", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-26T19:50:46+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #phi #text-generation #custom_code #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. 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(2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #phi #text-generation #custom_code #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. 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More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # GUE_prom_prom_core_notata-seqsight_4096_512_46M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_notata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_notata) dataset. It achieves the following results on the evaluation set: - Loss: 0.3807 - F1 Score: 0.8364 - Accuracy: 0.8364 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.47 \| 0.6 \| 200 \| 0.4062 \| 0.8137 \| 0.8144 \| \| 0.405 \| 1.2 \| 400 \| 0.3753 \| 0.8331 \| 0.8332 \| \| 0.3902 \| 1.81 \| 600 \| 0.3661 \| 0.8386 \| 0.8387 \| \| 0.3818 \| 2.41 \| 800 \| 0.3680 \| 0.8363 \| 0.8363 \| \| 0.3757 \| 3.01 \| 1000 \| 0.3655 \| 0.8385 \| 0.8387 \| \| 0.3689 \| 3.61 \| 1200 \| 0.3578 \| 0.8413 \| 0.8413 \| \| 0.3703 \| 4.22 \| 1400 \| 0.3616 \| 0.8397 \| 0.8396 \| \| 0.3694 \| 4.82 \| 1600 \| 0.3579 \| 0.8394 \| 0.8395 \| \| 0.3556 \| 5.42 \| 1800 \| 0.3794 \| 0.8334 \| 0.8336 \| \| 0.3611 \| 6.02 \| 2000 \| 0.3611 \| 0.8407 \| 0.8408 \| \| 0.3539 \| 6.63 \| 2200 \| 0.3585 \| 0.8430 \| 0.8430 \| \| 0.3493 \| 7.23 \| 2400 \| 0.3640 \| 0.8451 \| 0.8453 \| \| 0.3511 \| 7.83 \| 2600 \| 0.3590 \| 0.8455 \| 0.8455 \| \| 0.3419 \| 8.43 \| 2800 \| 0.3626 \| 0.8412 \| 0.8415 \| \| 0.3473 \| 9.04 \| 3000 \| 0.3592 \| 0.8443 \| 0.8444 \| \| 0.3401 \| 9.64 \| 3200 \| 0.3644 \| 0.8464 \| 0.8464 \| \| 0.3358 \| 10.24 \| 3400 \| 0.3688 \| 0.8462 \| 0.8462 \| \| 0.3351 \| 10.84 \| 3600 \| 0.3579 \| 0.8440 \| 0.8440 \| \| 0.3314 \| 11.45 \| 3800 \| 0.3599 \| 0.8427 \| 0.8427 \| \| 0.3321 \| 12.05 \| 4000 \| 0.3602 \| 0.8457 \| 0.8457 \| \| 0.3235 \| 12.65 \| 4200 \| 0.3640 \| 0.8466 \| 0.8466 \| \| 0.3256 \| 13.25 \| 4400 \| 0.3656 \| 0.8436 \| 0.8438 \| \| 0.3269 \| 13.86 \| 4600 \| 0.3658 \| 0.8456 \| 0.8457 \| \| 0.3203 \| 14.46 \| 4800 \| 0.3698 \| 0.8444 \| 0.8445 \| \| 0.3176 \| 15.06 \| 5000 \| 0.3608 \| 0.8449 \| 0.8449 \| \| 0.3121 \| 15.66 \| 5200 \| 0.3632 \| 0.8458 \| 0.8459 \| \| 0.3149 \| 16.27 \| 5400 \| 0.3695 \| 0.8465 \| 0.8466 \| \| 0.3116 \| 16.87 \| 5600 \| 0.3806 \| 0.8414 \| 0.8419 \| \| 0.306 \| 17.47 \| 5800 \| 0.3744 \| 0.8403 \| 0.8406 \| \| 0.3172 \| 18.07 \| 6000 \| 0.3707 \| 0.8453 \| 0.8457 \| \| 0.3045 \| 18.67 \| 6200 \| 0.3712 \| 0.8458 \| 0.8459 \| \| 0.3058 \| 19.28 \| 6400 \| 0.3723 \| 0.8437 \| 0.8438 \| \| 0.3024 \| 19.88 \| 6600 \| 0.3660 \| 0.8462 \| 0.8462 \| \| 0.2957 \| 20.48 \| 6800 \| 0.3684 \| 0.8455 \| 0.8457 \| \| 0.3049 \| 21.08 \| 7000 \| 0.3726 \| 0.8426 \| 0.8428 \| \| 0.3004 \| 21.69 \| 7200 \| 0.3743 \| 0.8449 \| 0.8449 \| \| 0.2911 \| 22.29 \| 7400 \| 0.3723 \| 0.8417 \| 0.8419 \| \| 0.2934 \| 22.89 \| 7600 \| 0.3815 \| 0.8423 \| 0.8425 \| \| 0.2913 \| 23.49 \| 7800 \| 0.3742 \| 0.8429 \| 0.8430 \| \| 0.2947 \| 24.1 \| 8000 \| 0.3811 \| 0.8436 \| 0.8438 \| \| 0.2925 \| 24.7 \| 8200 \| 0.3807 \| 0.8405 \| 0.8408 \| \| 0.2832 \| 25.3 \| 8400 \| 0.3903 \| 0.8431 \| 0.8432 \| \| 0.2904 \| 25.9 \| 8600 \| 0.3788 \| 0.8433 \| 0.8434 \| \| 0.2891 \| 26.51 \| 8800 \| 0.3780 \| 0.8449 \| 0.8449 \| \| 0.2876 \| 27.11 \| 9000 \| 0.3794 \| 0.8415 \| 0.8415 \| \| 0.2877 \| 27.71 \| 9200 \| 0.3848 \| 0.8414 \| 0.8415 \| \| 0.2785 \| 28.31 \| 9400 \| 0.3852 \| 0.8391 \| 0.8393 \| \| 0.2839 \| 28.92 \| 9600 \| 0.3865 \| 0.8421 \| 0.8423 \| \| 0.2825 \| 29.52 \| 9800 \| 0.3854 \| 0.8410 \| 0.8412 \| \| 0.2791 \| 30.12 \| 10000 \| 0.3856 \| 0.8420 \| 0.8421 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_prom_prom_core_notata-seqsight_4096_512_46M-L32_f", "results": []}]}	mahdibaghbanzadeh/GUE_prom_prom_core_notata-seqsight_4096_512_46M-L32_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_46M", "region:us" ]	null	2024-04-26T19:52:02+00:00	[]	[]	TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us	GUE\_prom\_prom\_core\_notata-seqsight\_4096\_512\_46M-L32\_f ============================================================= This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_46M on the mahdibaghbanzadeh/GUE\_prom\_prom\_core\_notata dataset. It achieves the following results on the evaluation set: * Loss: 0.3807 * F1 Score: 0.8364 * Accuracy: 0.8364 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]	[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]
text-generation	transformers	<div align="center"> <img width="260px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/BrQCb95lmEIFz79QAmoNA.png"></div> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/2FhDh8NDvMl7iSxbQz9BP.png) <div align="center"> <h1>Advancing Open-source Large Language Models in Medical Domain</h1> </div> <p align="center" style="margin-top: 0px;"> <a href="https://colab.research.google.com/drive/1F5oV20InEYeAJGmBwYF9NM_QhLmjBkKJ?usp=sharing"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="OpenChat Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 10px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">Online Demo</span> </a> \| <a href="https://github.com/openlifescience-ai"> <img src="https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png" alt="GitHub Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">GitHub</span> </a> \| <a href="#"> <img src="https://github.com/alpayariyak/openchat/blob/master/assets/arxiv-logomark-small-square-border.png?raw=true" alt="ArXiv Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style="margin-right: 5px;">Paper</span> </a> \| <a href="https://discord.gg/A5Fjf5zC69"> <img src="https://cloud.githubusercontent.com/assets/6291467/26705903/96c2d66e-477c-11e7-9f4e-f3c0efe96c9a.png" alt="Discord Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text">Discord</span> </a> </p> ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/KGmRE5w2sepNtwsEu8t7K.jpeg) Introducing OpenBioLLM-8B: A State-of-the-Art Open Source Biomedical Large Language Model OpenBioLLM-8B is an advanced open source language model designed specifically for the biomedical domain. Developed by Saama AI Labs, this model leverages cutting-edge techniques to achieve state-of-the-art performance on a wide range of biomedical tasks. 🏥 Biomedical Specialization: OpenBioLLM-8B is tailored for the unique language and knowledge requirements of the medical and life sciences fields. It was fine-tuned on a vast corpus of high-quality biomedical data, enabling it to understand and generate text with domain-specific accuracy and fluency. 🎓 Superior Performance: With 8 billion parameters, OpenBioLLM-8B outperforms other open source biomedical language models of similar scale. It has also demonstrated better results compared to larger proprietary & open-source models like GPT-3.5 and Meditron-70B on biomedical benchmarks. 🧠 Advanced Training Techniques: OpenBioLLM-8B builds upon the powerful foundations of the Meta-Llama-3-8B and [Meta-Llama-3-8B](meta-llama/Meta-Llama-3-8B) models. It incorporates the DPO dataset and fine-tuning recipe along with a custom diverse medical instruction dataset. Key components of the training pipeline include: <div align="center"> <img width="1200px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/oPchsJsEpQoGcGXVbh7YS.png"> </div> - Policy Optimization: [Direct Preference Optimization: Your Language Model is Secretly a Reward Model (DPO)](https://arxiv.org/abs/2305.18290) - Ranking Dataset: [berkeley-nest/Nectar](https://huggingface.co/datasets/berkeley-nest/Nectar) - Fine-tuning dataset: Custom Medical Instruct dataset (We plan to release a sample training dataset in our upcoming paper; please stay updated) This combination of cutting-edge techniques enables OpenBioLLM-8B to align with key capabilities and preferences for biomedical applications. ⚙️ Release Details: - Model Size: 8 billion parameters - Quantization: Optimized quantized versions available [Here](https://huggingface.co/aaditya/OpenBioLLM-8B-GGUF) - Language(s) (NLP): en - Developed By: [Ankit Pal (Aaditya Ura)](https://aadityaura.github.io/) from Saama AI Labs - License: Meta-Llama License - Fine-tuned from models: [meta-llama/Meta-Llama-3-8B](meta-llama/Meta-Llama-3-8B) - Resources for more information: - Paper: Coming soon The model can be fine-tuned for more specialized tasks and datasets as needed. OpenBioLLM-8B represents an important step forward in democratizing advanced language AI for the biomedical community. By leveraging state-of-the-art architectures and training techniques from leading open source efforts like Llama-3, we have created a powerful tool to accelerate innovation and discovery in healthcare and the life sciences. We are excited to share OpenBioLLM-8B with researchers and developers around the world. ### Use with transformers Important: Please use the exact chat template provided by Llama-3 instruct version. Otherwise there will be a degradation in the performance. The model output can be verbose in rare cases. Please consider setting temperature = 0 to make this happen less. See the snippet below for usage with Transformers: ```python import transformers import torch model_id = "aaditya/OpenBioLLM-Llama3-8B" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device="auto", ) messages = [ {"role": "system", "content": "You are an expert and experienced from the healthcare and biomedical domain with extensive medical knowledge and practical experience. Your name is OpenBioLLM, and you were developed by Saama AI Labs. who's willing to help answer the user's query with explanation. In your explanation, leverage your deep medical expertise such as relevant anatomical structures, physiological processes, diagnostic criteria, treatment guidelines, or other pertinent medical concepts. Use precise medical terminology while still aiming to make the explanation clear and accessible to a general audience."}, {"role": "user", "content": "How can i split a 3mg or 4mg waefin pill so i can get a 2.5mg pill?"}, ] prompt = pipeline.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) terminators = [ pipeline.tokenizer.eos_token_id, pipeline.tokenizer.convert_tokens_to_ids("<\|eot_id\|>") ] outputs = pipeline( prompt, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.0, top_p=0.9, ) print(outputs[0]["generated_text"][len(prompt):]) ``` ## Training procedure ### Training hyperparameters <details> <summary>Click to see details</summary> - learning_rate: 0.0002 - lr_scheduler: cosine - train_batch_size: 12 - eval_batch_size: 8 - GPU: H100 80GB SXM5 - num_devices: 1 - optimizer: adamw_bnb_8bit - lr_scheduler_warmup_steps: 100 - num_epochs: 4 </details> ### Peft hyperparameters <details> <summary>Click to see details</summary> - adapter: qlora - lora_r: 128 - lora_alpha: 256 - lora_dropout: 0.05 - lora_target_linear: true -lora_target_modules: - q_proj - v_proj - k_proj - o_proj - gate_proj - down_proj - up_proj </details> ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.1 - Axolotl - Lm harness for evaluation # Benchmark Results 🔥 OpenBioLLM-8B demonstrates superior performance compared to larger models, such as GPT-3.5, Meditron-70B across 9 diverse biomedical datasets, achieving state-of-the-art results with an average score of 72.50%, despite having a significantly smaller parameter count. The model's strong performance in domain-specific tasks, such as Clinical KG, Medical Genetics, and PubMedQA, highlights its ability to effectively capture and apply biomedical knowledge. 🚨 The GPT-4, Med-PaLM-1, and Med-PaLM-2 results are taken from their official papers. Since Med-PaLM doesn't provide zero-shot accuracy, we are using 5-shot accuracy from their paper for comparison. All results presented are in the zero-shot setting, except for Med-PaLM-2 and Med-PaLM-1, which use 5-shot accuracy. \| \| Clinical KG \| Medical Genetics \| Anatomy \| Pro Medicine \| College Biology \| College Medicine \| MedQA 4 opts \| PubMedQA \| MedMCQA \| Avg \| \|--------------------\|-------------\|------------------\|---------\|--------------\|-----------------\|------------------\|--------------\|----------\|---------\|-------\| \| OpenBioLLM-70B \| 92.93 \| 93.197 \| 83.904 \| 93.75 \| 93.827 \| 85.749 \| 78.162 \| 78.97 \| 74.014 \| 86.05588 \| \| Med-PaLM-2 (5-shot) \| 88.3 \| 90 \| 77.8 \| 95.2 \| 94.4 \| 80.9 \| 79.7 \| 79.2 \| 71.3 \| 84.08 \| \| GPT-4 \| 86.04 \| 91 \| 80 \| 93.01 \| 95.14 \| 76.88 \| 78.87 \| 75.2 \| 69.52 \| 82.85 \| \| Med-PaLM-1 (Flan-PaLM, 5-shot) \| 80.4 \| 75 \| 63.7 \| 83.8 \| 88.9 \| 76.3 \| 67.6 \| 79 \| 57.6 \| 74.7 \| \| OpenBioLLM-8B \| 76.101 \| 86.1 \| 69.829 \| 78.21 \| 84.213 \| 68.042 \| 58.993 \| 74.12 \| 56.913 \| 72.502 \| \| Gemini-1.0 \| 76.7 \| 75.8 \| 66.7 \| 77.7 \| 88 \| 69.2 \| 58 \| 70.7 \| 54.3 \| 70.79 \| \| GPT-3.5 Turbo 1106 \| 74.71 \| 74 \| 72.79 \| 72.79 \| 72.91 \| 64.73 \| 57.71 \| 72.66 \| 53.79 \| 66 \| \| Meditron-70B \| 66.79 \| 69 \| 53.33 \| 71.69 \| 76.38 \| 63 \| 57.1 \| 76.6 \| 46.85 \| 64.52 \| \| gemma-7b \| 69.81 \| 70 \| 59.26 \| 66.18 \| 79.86 \| 60.12 \| 47.21 \| 76.2 \| 48.96 \| 64.18 \| \| Mistral-7B-v0.1 \| 68.68 \| 71 \| 55.56 \| 68.38 \| 68.06 \| 59.54 \| 50.82 \| 75.4 \| 48.2 \| 62.85 \| \| Apollo-7B \| 62.26 \| 72 \| 61.48 \| 69.12 \| 70.83 \| 55.49 \| 55.22 \| 39.8 \| 53.77 \| 60 \| \| MedAlpaca-7b \| 57.36 \| 69 \| 57.04 \| 67.28 \| 65.28 \| 54.34 \| 41.71 \| 72.8 \| 37.51 \| 58.03 \| \| BioMistral-7B \| 59.9 \| 64 \| 56.5 \| 60.4 \| 59 \| 54.7 \| 50.6 \| 77.5 \| 48.1 \| 57.3 \| \| AlpaCare-llama2-7b \| 49.81 \| 49 \| 45.92 \| 33.82 \| 50 \| 43.35 \| 29.77 \| 72.2 \| 34.42 \| 45.36 \| \| ClinicalGPT \| 30.56 \| 27 \| 30.37 \| 19.48 \| 25 \| 24.27 \| 26.08 \| 63.8 \| 28.18 \| 30.52 \| <div align="center"> <img width="1600px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/_SzdcJSBjZyo8RS1bTEkP.png"> </div> ## Detailed Medical Subjectwise accuracy ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/UXF-V0col0Z0sS6BGPBkE.png) # Use Cases & Examples 🚨 Below results are from the quantized version of OpenBioLLM-70B # Summarize Clinical Notes OpenBioLLM-70B can efficiently analyze and summarize complex clinical notes, EHR data, and discharge summaries, extracting key information and generating concise, structured summaries ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/xdwdBgOxNi_TfML0hKlI8.png) # Answer Medical Questions OpenBioLLM-70B can provide answers to a wide range of medical questions. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/zO95GlwOQEZqCKQF69mE6.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/OKBczKw7gWeW5xsuDpc27.png) <details> <summary>Click to see details</summary> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/eJGHT5khppYvJb8fQ-YW4.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/Cnbwrqa_-ORHRuNRC2P6Y.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/J9DhdcvukAc9mnnW9fj2C.png) </details> # Clinical Entity Recognition OpenBioLLM-70B can perform advanced clinical entity recognition by identifying and extracting key medical concepts, such as diseases, symptoms, medications, procedures, and anatomical structures, from unstructured clinical text. By leveraging its deep understanding of medical terminology and context, the model can accurately annotate and categorize clinical entities, enabling more efficient information retrieval, data analysis, and knowledge discovery from electronic health records, research articles, and other biomedical text sources. This capability can support various downstream applications, such as clinical decision support, pharmacovigilance, and medical research. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/_69BW4k9LVABFwtxixL45.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/DKy5wYCoPhoPPUc1-x8_J.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/7WD9zCCBZT4-4XlfnIQjl.png) # Biomarkers Extraction ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/ZttoM4AiteT7gFYVhjIpN.png) # Classification OpenBioLLM-70B can perform various biomedical classification tasks, such as disease prediction, sentiment analysis, medical document categorization ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/Bf5MW1d75qT-1F_TR_hC0.png) # De-Identification OpenBioLLM-70B can detect and remove personally identifiable information (PII) from medical records, ensuring patient privacy and compliance with data protection regulations like HIPAA. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/hKX4kzm--Tw5bj6K78msy.png) Advisory Notice! While OpenBioLLM-70B & 8B leverages high-quality data sources, its outputs may still contain inaccuracies, biases, or misalignments that could pose risks if relied upon for medical decision-making without further testing and refinement. The model's performance has not yet been rigorously evaluated in randomized controlled trials or real-world healthcare environments. Therefore, we strongly advise against using OpenBioLLM-70B & 8B for any direct patient care, clinical decision support, or other professional medical purposes at this time. Its use should be limited to research, development, and exploratory applications by qualified individuals who understand its limitations. OpenBioLLM-70B & 8B are intended solely as a research tool to assist healthcare professionals and should never be considered a replacement for the professional judgment and expertise of a qualified medical doctor. Appropriately adapting and validating OpenBioLLM-70B & 8B for specific medical use cases would require significant additional work, potentially including: - Thorough testing and evaluation in relevant clinical scenarios - Alignment with evidence-based guidelines and best practices - Mitigation of potential biases and failure modes - Integration with human oversight and interpretation - Compliance with regulatory and ethical standards Always consult a qualified healthcare provider for personal medical needs. # Citation If you find OpenBioLLM-70B & 8B useful in your work, please cite the model as follows: ``` @misc{OpenBioLLMs, author = {Ankit Pal, Malaikannan Sankarasubbu}, title = {OpenBioLLMs: Advancing Open-Source Large Language Models for Healthcare and Life Sciences}, year = {2024}, publisher = {Hugging Face}, journal = {Hugging Face repository}, howpublished = {\url{https://huggingface.co/aaditya/OpenBioLLM-Llama3-70B}} } ``` The accompanying paper is currently in progress and will be released soon. <div align="center"> <h2> 💌 Contact </h2> </div> We look forward to hearing you and collaborating on this exciting project! Contributors: - [Ankit Pal (Aaditya Ura)](https://aadityaura.github.io/) [aadityaura at gmail dot com] - Saama AI Labs - Note: I am looking for a funded PhD opportunity, especially if it fits my Responsible Generative AI, Multimodal LLMs, Geometric Deep Learning, and Healthcare AI skillset. # References We thank the [Meta Team](meta-llama/Meta-Llama-3-70B-Instruct) for their amazing models! Result sources - [1] GPT-4 [Capabilities of GPT-4 on Medical Challenge Problems] (https://arxiv.org/abs/2303.13375) - [2] Med-PaLM-1 [Large Language Models Encode Clinical Knowledge](https://arxiv.org/abs/2212.13138) - [3] Med-PaLM-2 [Towards Expert-Level Medical Question Answering with Large Language Models](https://arxiv.org/abs/2305.09617) - [4] Gemini-1.0 [Gemini Goes to Med School](https://arxiv.org/abs/2402.07023)	{"language": ["en"], "license": "llama3", "tags": ["llama-3", "llama", "Mixtral", "instruct", "finetune", "chatml", "DPO", "RLHF", "gpt4", "distillation"], "base_model": "meta-llama/Meta-Llama-3-8B", "widget": [{"example_title": "OpenBioLLM-8B", "messages": [{"role": "system", "content": "You are an expert and experienced from the healthcare and biomedical domain with extensive medical knowledge and practical experience."}, {"role": "user", "content": "How long does it take for newborn jaundice to go away?"}], "output": {"text": "Newborn jaundice, also known as neonatal jaundice, is a common condition in newborns where the yellowing of the skin and eyes occurs due to an elevated level of bilirubin in the blood. Bilirubin is a yellow pigment that forms when red blood cells break down. In most cases, newborn jaundice resolves on its own without any specific treatment.\nThe duration of newborn jaundice can vary depending on several factors such as the underlying cause, gestational age at birth, and individual variations in bilirubin metabolism. Here are some general guidelines:\n1. Physiological jaundice: This is the most common type of newborn jaundice and usually appears within 24-72 hours after birth. It tends to peak between the second and fifth day of life and gradually improves over the next week or two. By the time the baby is one week old, the jaundice should have mostly resolved. 2. Breast milk jaundice: This type of jaundice occurs in breastfed babies and may appear later than physiological jaundice, typically between the fifth and fourteenth day of life. It tends to persist for a longer duration but usually resolves within six weeks after birth. 3. Pathological jaundice: This type of jaundice is less common and occurs due to an underlying medical condition that affects bilirubin metabolism or liver function. The duration of pathological jaundice depends on the specific cause and may require treatment.\nIt's important for parents to monitor their newborn's jaundice closely and seek medical advice if the jaundice progresses rapidly, becomes severe, or is accompanied by other symptoms such as poor feeding, lethargy, or excessive sleepiness. In these cases, further evaluation and management may be necessary. Remember that each baby is unique, and the timing of jaundice resolution can vary. If you have concerns about your newborn's jaundice, it's always best to consult with a healthcare professional for personalized advice and guidance."}}], "model-index": [{"name": "OpenBioLLM-8B", "results": []}]}	LoneStriker/OpenBioLLM-Llama3-8B-8.0bpw-h8-exl2	null	[ "transformers", "pytorch", "llama", "text-generation", "llama-3", "Mixtral", "instruct", "finetune", "chatml", "DPO", "RLHF", "gpt4", "distillation", "en", "arxiv:2305.18290", "arxiv:2303.13375", "arxiv:2212.13138", "arxiv:2305.09617", "arxiv:2402.07023", "base_model:meta-llama/Meta-Llama-3-8B", "license:llama3", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "8-bit", "region:us" ]	null	2024-04-26T19:52:20+00:00	[ "2305.18290", "2303.13375", "2212.13138", "2305.09617", "2402.07023" ]	[ "en" ]	TAGS #transformers #pytorch #llama #text-generation #llama-3 #Mixtral #instruct #finetune #chatml #DPO #RLHF #gpt4 #distillation #en #arxiv-2305.18290 #arxiv-2303.13375 #arxiv-2212.13138 #arxiv-2305.09617 #arxiv-2402.07023 #base_model-meta-llama/Meta-Llama-3-8B #license-llama3 #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us	![](URL <p>!image/png</p> <div align=) Advancing Open-source Large Language Models in Medical Domain ============================================================= Online Demo \| GitHub \| [![](URL alt=)](#) \| Discord !image/jpeg Introducing OpenBioLLM-8B: A State-of-the-Art Open Source Biomedical Large Language Model OpenBioLLM-8B is an advanced open source language model designed specifically for the biomedical domain. Developed by Saama AI Labs, this model leverages cutting-edge techniques to achieve state-of-the-art performance on a wide range of biomedical tasks. Biomedical Specialization: OpenBioLLM-8B is tailored for the unique language and knowledge requirements of the medical and life sciences fields. It was fine-tuned on a vast corpus of high-quality biomedical data, enabling it to understand and generate text with domain-specific accuracy and fluency. Superior Performance: With 8 billion parameters, OpenBioLLM-8B outperforms other open source biomedical language models of similar scale. It has also demonstrated better results compared to larger proprietary & open-source models like GPT-3.5 and Meditron-70B on biomedical benchmarks. Advanced Training Techniques: OpenBioLLM-8B builds upon the powerful foundations of the Meta-Llama-3-8B and Meta-Llama-3-8B models. It incorporates the DPO dataset and fine-tuning recipe along with a custom diverse medical instruction dataset. Key components of the training pipeline include: ![](URL </div> <ul> <li>Policy Optimization: Direct Preference Optimization: Your Language Model is Secretly a Reward Model (DPO)</li> <li>Ranking Dataset: berkeley-nest/Nectar</li> <li>Fine-tuning dataset: Custom Medical Instruct dataset (We plan to release a sample training dataset in our upcoming paper; please stay updated)</li> </ul> <p>This combination of cutting-edge techniques enables OpenBioLLM-8B to align with key capabilities and preferences for biomedical applications.</p> <p>️ Release Details:</p> <ul> <li>Model Size: 8 billion parameters</li> <li>Quantization: Optimized quantized versions available Here</li> <li>Language(s) (NLP): en</li> <li>Developed By: Ankit Pal (Aaditya Ura) from Saama AI Labs</li> <li>License: Meta-Llama License</li> <li>Fine-tuned from models: meta-llama/Meta-Llama-3-8B</li> <li>Resources for more information: <ul> <li>Paper: Coming soon</li> </ul> </li> </ul> <p>The model can be fine-tuned for more specialized tasks and datasets as needed.</p> <p>OpenBioLLM-8B represents an important step forward in democratizing advanced language AI for the biomedical community. By leveraging state-of-the-art architectures and training techniques from leading open source efforts like Llama-3, we have created a powerful tool to accelerate innovation and discovery in healthcare and the life sciences.</p> <p>We are excited to share OpenBioLLM-8B with researchers and developers around the world.</p> <h3>Use with transformers</h3> <p>Important: Please use the exact chat template provided by Llama-3 instruct version. Otherwise there will be a degradation in the performance. The model output can be verbose in rare cases. Please consider setting temperature = 0 to make this happen less.</p> <p>See the snippet below for usage with Transformers:</p> <h2>Training procedure</h2> <h3>Training hyperparameters</h3> <details> <summary>Click to see details</summary> <ul> <li>learning_rate: 0.0002</li> <li>lr_scheduler: cosine</li> <li>train_batch_size: 12</li> <li>eval_batch_size: 8</li> <li>GPU: H100 80GB SXM5</li> <li>num_devices: 1</li> <li>optimizer: adamw_bnb_8bit</li> <li>lr_scheduler_warmup_steps: 100</li> <li>num_epochs: 4</li> </ul> </details> <h3>Peft hyperparameters</h3> <details> <summary>Click to see details</summary> <ul> <li>adapter: qlora</li> <li>lora_r: 128</li> <li>lora_alpha: 256</li> <li>lora_dropout: 0.05</li> <li>lora_target_linear: true</li> </ul> <p>-lora_target_modules:</p> <ul> <li>q_proj</li> <li>v_proj</li> <li>k_proj</li> <li>o_proj</li> <li>gate_proj</li> <li>down_proj</li> <li>up_proj</li> </ul> </details> <h3>Training results</h3> <h3>Framework versions</h3> <ul> <li>Transformers 4.39.3</li> <li>Pytorch 2.1.2+cu121</li> <li>Datasets 2.18.0</li> <li>Tokenizers 0.15.1</li> <li>Axolotl</li> <li>Lm harness for evaluation</li> </ul> <h1>Benchmark Results</h1> <p>OpenBioLLM-8B demonstrates superior performance compared to larger models, such as GPT-3.5, Meditron-70B across 9 diverse biomedical datasets, achieving state-of-the-art results with an average score of 72.50%, despite having a significantly smaller parameter count. The model's strong performance in domain-specific tasks, such as Clinical KG, Medical Genetics, and PubMedQA, highlights its ability to effectively capture and apply biomedical knowledge.</p> <p>The GPT-4, Med-PaLM-1, and Med-PaLM-2 results are taken from their official papers. Since Med-PaLM doesn't provide zero-shot accuracy, we are using 5-shot accuracy from their paper for comparison. All results presented are in the zero-shot setting, except for Med-PaLM-2 and Med-PaLM-1, which use 5-shot accuracy.</p> <p></p> <div align=) ![](URL </div> <h2>Detailed Medical Subjectwise accuracy</h2> <p>!image/png</p> <h1>Use Cases & Examples</h1> <p>Below results are from the quantized version of OpenBioLLM-70B</p> <h1>Summarize Clinical Notes</h1> <p>OpenBioLLM-70B can efficiently analyze and summarize complex clinical notes, EHR data, and discharge summaries, extracting key information and generating concise, structured summaries</p> <p>!image/png</p> <h1>Answer Medical Questions</h1> <p>OpenBioLLM-70B can provide answers to a wide range of medical questions.</p> <p>!image/png !image/png</p> <details> <summary>Click to see details</summary> <p>!image/png !image/png !image/png</p> </details> <h1>Clinical Entity Recognition</h1> <p>OpenBioLLM-70B can perform advanced clinical entity recognition by identifying and extracting key medical concepts, such as diseases, symptoms, medications, procedures, and anatomical structures, from unstructured clinical text. By leveraging its deep understanding of medical terminology and context, the model can accurately annotate and categorize clinical entities, enabling more efficient information retrieval, data analysis, and knowledge discovery from electronic health records, research articles, and other biomedical text sources. This capability can support various downstream applications, such as clinical decision support, pharmacovigilance, and medical research.</p> <p>!image/png !image/png !image/png</p> <h1>Biomarkers Extraction</h1> <p>!image/png</p> <h1>Classification</h1> <p>OpenBioLLM-70B can perform various biomedical classification tasks, such as disease prediction, sentiment analysis, medical document categorization</p> <p>!image/png</p> <h1>De-Identification</h1> <p>OpenBioLLM-70B can detect and remove personally identifiable information (PII) from medical records, ensuring patient privacy and compliance with data protection regulations like HIPAA.</p> <p>!image/png</p> <p>Advisory Notice!</p> <p>While OpenBioLLM-70B & 8B leverages high-quality data sources, its outputs may still contain inaccuracies, biases, or misalignments that could pose risks if relied upon for medical decision-making without further testing and refinement. The model's performance has not yet been rigorously evaluated in randomized controlled trials or real-world healthcare environments.</p> <p>Therefore, we strongly advise against using OpenBioLLM-70B & 8B for any direct patient care, clinical decision support, or other professional medical purposes at this time. Its use should be limited to research, development, and exploratory applications by qualified individuals who understand its limitations. OpenBioLLM-70B & 8B are intended solely as a research tool to assist healthcare professionals and should never be considered a replacement for the professional judgment and expertise of a qualified medical doctor.</p> <p>Appropriately adapting and validating OpenBioLLM-70B & 8B for specific medical use cases would require significant additional work, potentially including:</p> <ul> <li>Thorough testing and evaluation in relevant clinical scenarios</li> <li>Alignment with evidence-based guidelines and best practices</li> <li>Mitigation of potential biases and failure modes</li> <li>Integration with human oversight and interpretation</li> <li>Compliance with regulatory and ethical standards</li> </ul> <p>Always consult a qualified healthcare provider for personal medical needs.</p> <p>If you find OpenBioLLM-70B & 8B useful in your work, please cite the model as follows:</p> <p>The accompanying paper is currently in progress and will be released soon.</p> <div align=) Contact -------- We look forward to hearing you and collaborating on this exciting project! Contributors: * Ankit Pal (Aaditya Ura) [aadityaura at gmail dot com] * Saama AI Labs * Note: I am looking for a funded PhD opportunity, especially if it fits my Responsible Generative AI, Multimodal LLMs, Geometric Deep Learning, and Healthcare AI skillset. References ========== We thank the Meta Team for their amazing models! Result sources * [1] GPT-4 [Capabilities of GPT-4 on Medical Challenge Problems] (URL * [2] Med-PaLM-1 Large Language Models Encode Clinical Knowledge * [3] Med-PaLM-2 Towards Expert-Level Medical Question Answering with Large Language Models * [4] Gemini-1.0 Gemini Goes to Med School	[]	[ "TAGS\n#transformers #pytorch #llama #text-generation #llama-3 #Mixtral #instruct #finetune #chatml #DPO #RLHF #gpt4 #distillation #en #arxiv-2305.18290 #arxiv-2303.13375 #arxiv-2212.13138 #arxiv-2305.09617 #arxiv-2402.07023 #base_model-meta-llama/Meta-Llama-3-8B #license-llama3 #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us \n" ]
null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # GUE_prom_prom_core_tata-seqsight_4096_512_46M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_tata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_tata) dataset. It achieves the following results on the evaluation set: - Loss: 0.4677 - F1 Score: 0.8303 - Accuracy: 0.8303 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.564 \| 5.13 \| 200 \| 0.5603 \| 0.7059 \| 0.7064 \| \| 0.5228 \| 10.26 \| 400 \| 0.5456 \| 0.7313 \| 0.7325 \| \| 0.4936 \| 15.38 \| 600 \| 0.5083 \| 0.7529 \| 0.7537 \| \| 0.459 \| 20.51 \| 800 \| 0.4685 \| 0.7673 \| 0.7684 \| \| 0.4227 \| 25.64 \| 1000 \| 0.4269 \| 0.8025 \| 0.8026 \| \| 0.3929 \| 30.77 \| 1200 \| 0.4184 \| 0.8203 \| 0.8206 \| \| 0.3703 \| 35.9 \| 1400 \| 0.4158 \| 0.8204 \| 0.8206 \| \| 0.3566 \| 41.03 \| 1600 \| 0.3927 \| 0.8400 \| 0.8401 \| \| 0.3452 \| 46.15 \| 1800 \| 0.3935 \| 0.8385 \| 0.8385 \| \| 0.33 \| 51.28 \| 2000 \| 0.3986 \| 0.8368 \| 0.8369 \| \| 0.3209 \| 56.41 \| 2200 \| 0.3908 \| 0.8433 \| 0.8434 \| \| 0.3114 \| 61.54 \| 2400 \| 0.3818 \| 0.8449 \| 0.8450 \| \| 0.3025 \| 66.67 \| 2600 \| 0.3809 \| 0.8531 \| 0.8532 \| \| 0.2974 \| 71.79 \| 2800 \| 0.3810 \| 0.8515 \| 0.8515 \| \| 0.278 \| 76.92 \| 3000 \| 0.3911 \| 0.8548 \| 0.8548 \| \| 0.2771 \| 82.05 \| 3200 \| 0.3951 \| 0.8385 \| 0.8385 \| \| 0.2645 \| 87.18 \| 3400 \| 0.4001 \| 0.8434 \| 0.8434 \| \| 0.2592 \| 92.31 \| 3600 \| 0.4055 \| 0.8562 \| 0.8564 \| \| 0.2448 \| 97.44 \| 3800 \| 0.4128 \| 0.8513 \| 0.8515 \| \| 0.2415 \| 102.56 \| 4000 \| 0.4101 \| 0.8531 \| 0.8532 \| \| 0.2343 \| 107.69 \| 4200 \| 0.4071 \| 0.8449 \| 0.8450 \| \| 0.2232 \| 112.82 \| 4400 \| 0.4219 \| 0.8463 \| 0.8467 \| \| 0.2209 \| 117.95 \| 4600 \| 0.4118 \| 0.8514 \| 0.8515 \| \| 0.2116 \| 123.08 \| 4800 \| 0.4258 \| 0.8532 \| 0.8532 \| \| 0.2072 \| 128.21 \| 5000 \| 0.4340 \| 0.8578 \| 0.8581 \| \| 0.2006 \| 133.33 \| 5200 \| 0.4217 \| 0.8547 \| 0.8548 \| \| 0.1946 \| 138.46 \| 5400 \| 0.4435 \| 0.8430 \| 0.8434 \| \| 0.185 \| 143.59 \| 5600 \| 0.4495 \| 0.8482 \| 0.8483 \| \| 0.183 \| 148.72 \| 5800 \| 0.4562 \| 0.8399 \| 0.8401 \| \| 0.1738 \| 153.85 \| 6000 \| 0.4683 \| 0.8495 \| 0.8499 \| \| 0.1735 \| 158.97 \| 6200 \| 0.4558 \| 0.8546 \| 0.8548 \| \| 0.17 \| 164.1 \| 6400 \| 0.4687 \| 0.8564 \| 0.8564 \| \| 0.1651 \| 169.23 \| 6600 \| 0.4706 \| 0.8531 \| 0.8532 \| \| 0.1628 \| 174.36 \| 6800 \| 0.4622 \| 0.8515 \| 0.8515 \| \| 0.1592 \| 179.49 \| 7000 \| 0.4657 \| 0.8579 \| 0.8581 \| \| 0.1568 \| 184.62 \| 7200 \| 0.4697 \| 0.8564 \| 0.8564 \| \| 0.1531 \| 189.74 \| 7400 \| 0.4754 \| 0.8515 \| 0.8515 \| \| 0.1519 \| 194.87 \| 7600 \| 0.4839 \| 0.8481 \| 0.8483 \| \| 0.1456 \| 200.0 \| 7800 \| 0.4810 \| 0.8513 \| 0.8515 \| \| 0.1439 \| 205.13 \| 8000 \| 0.4818 \| 0.8433 \| 0.8434 \| \| 0.1409 \| 210.26 \| 8200 \| 0.4847 \| 0.8433 \| 0.8434 \| \| 0.1398 \| 215.38 \| 8400 \| 0.4923 \| 0.8481 \| 0.8483 \| \| 0.1384 \| 220.51 \| 8600 \| 0.4877 \| 0.8482 \| 0.8483 \| \| 0.1407 \| 225.64 \| 8800 \| 0.4909 \| 0.8400 \| 0.8401 \| \| 0.1375 \| 230.77 \| 9000 \| 0.4941 \| 0.8481 \| 0.8483 \| \| 0.1377 \| 235.9 \| 9200 \| 0.4932 \| 0.8450 \| 0.8450 \| \| 0.1371 \| 241.03 \| 9400 \| 0.4942 \| 0.8449 \| 0.8450 \| \| 0.1392 \| 246.15 \| 9600 \| 0.4937 \| 0.8417 \| 0.8418 \| \| 0.1329 \| 251.28 \| 9800 \| 0.4935 \| 0.8465 \| 0.8467 \| \| 0.1306 \| 256.41 \| 10000 \| 0.4939 \| 0.8481 \| 0.8483 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_prom_prom_core_tata-seqsight_4096_512_46M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_prom_prom_core_tata-seqsight_4096_512_46M-L1_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_46M", "region:us" ]	null	2024-04-26T19:57:34+00:00	[]	[]	TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us	GUE\_prom\_prom\_core\_tata-seqsight\_4096\_512\_46M-L1\_f ========================================================== This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_46M on the mahdibaghbanzadeh/GUE\_prom\_prom\_core\_tata dataset. It achieves the following results on the evaluation set: * Loss: 0.4677 * F1 Score: 0.8303 * Accuracy: 0.8303 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]	[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]
text-to-speech	null	![image/png](https://cdn-uploads.huggingface.co/production/uploads/662c0691b7c202c0841bcb4f/ZtIDazPj6z3It8KUkz03F.png) Wim Hof voice high quality RVC trained 300 epochs on interviews.	{"language": ["en"], "license": "afl-3.0", "tags": ["rvc", "speech", "tts", "voice model"], "pipeline_tag": "text-to-speech"}	Schnorchelgubby/WimHof	null	[ "rvc", "speech", "tts", "voice model", "text-to-speech", "en", "license:afl-3.0", "region:us" ]	null	2024-04-26T19:58:15+00:00	[]	[ "en" ]	TAGS #rvc #speech #tts #voice model #text-to-speech #en #license-afl-3.0 #region-us	!image/png Wim Hof voice high quality RVC trained 300 epochs on interviews.	[]	[ "TAGS\n#rvc #speech #tts #voice model #text-to-speech #en #license-afl-3.0 #region-us \n" ]
null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # GUE_prom_prom_core_tata-seqsight_4096_512_46M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_tata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_tata) dataset. It achieves the following results on the evaluation set: - Loss: 0.6392 - F1 Score: 0.8303 - Accuracy: 0.8303 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.5536 \| 5.13 \| 200 \| 0.5507 \| 0.7178 \| 0.7178 \| \| 0.4771 \| 10.26 \| 400 \| 0.4528 \| 0.7846 \| 0.7847 \| \| 0.3954 \| 15.38 \| 600 \| 0.4052 \| 0.8091 \| 0.8091 \| \| 0.3501 \| 20.51 \| 800 \| 0.4084 \| 0.8120 \| 0.8124 \| \| 0.3223 \| 25.64 \| 1000 \| 0.4058 \| 0.8278 \| 0.8287 \| \| 0.2912 \| 30.77 \| 1200 \| 0.4098 \| 0.8314 \| 0.8320 \| \| 0.2756 \| 35.9 \| 1400 \| 0.3914 \| 0.8384 \| 0.8385 \| \| 0.2552 \| 41.03 \| 1600 \| 0.3971 \| 0.8350 \| 0.8352 \| \| 0.2373 \| 46.15 \| 1800 \| 0.4074 \| 0.8365 \| 0.8369 \| \| 0.2217 \| 51.28 \| 2000 \| 0.4023 \| 0.8352 \| 0.8352 \| \| 0.2042 \| 56.41 \| 2200 \| 0.4607 \| 0.8334 \| 0.8336 \| \| 0.1924 \| 61.54 \| 2400 \| 0.4388 \| 0.8286 \| 0.8287 \| \| 0.1848 \| 66.67 \| 2600 \| 0.4548 \| 0.8349 \| 0.8352 \| \| 0.1709 \| 71.79 \| 2800 \| 0.4728 \| 0.8366 \| 0.8369 \| \| 0.1558 \| 76.92 \| 3000 \| 0.4994 \| 0.8352 \| 0.8352 \| \| 0.1493 \| 82.05 \| 3200 \| 0.5037 \| 0.8352 \| 0.8352 \| \| 0.1371 \| 87.18 \| 3400 \| 0.5434 \| 0.8401 \| 0.8401 \| \| 0.1331 \| 92.31 \| 3600 \| 0.5410 \| 0.8221 \| 0.8222 \| \| 0.1206 \| 97.44 \| 3800 \| 0.5585 \| 0.8432 \| 0.8434 \| \| 0.1183 \| 102.56 \| 4000 \| 0.5698 \| 0.8416 \| 0.8418 \| \| 0.1081 \| 107.69 \| 4200 \| 0.5582 \| 0.8417 \| 0.8418 \| \| 0.105 \| 112.82 \| 4400 \| 0.6159 \| 0.8401 \| 0.8401 \| \| 0.0991 \| 117.95 \| 4600 \| 0.6073 \| 0.8368 \| 0.8369 \| \| 0.094 \| 123.08 \| 4800 \| 0.6109 \| 0.8254 \| 0.8254 \| \| 0.0881 \| 128.21 \| 5000 \| 0.6315 \| 0.8352 \| 0.8352 \| \| 0.0883 \| 133.33 \| 5200 \| 0.6070 \| 0.8401 \| 0.8401 \| \| 0.0805 \| 138.46 \| 5400 \| 0.6284 \| 0.8433 \| 0.8434 \| \| 0.076 \| 143.59 \| 5600 \| 0.6523 \| 0.8319 \| 0.8320 \| \| 0.0798 \| 148.72 \| 5800 \| 0.6554 \| 0.8401 \| 0.8401 \| \| 0.0728 \| 153.85 \| 6000 \| 0.6709 \| 0.8466 \| 0.8467 \| \| 0.0701 \| 158.97 \| 6200 \| 0.6738 \| 0.8449 \| 0.8450 \| \| 0.0679 \| 164.1 \| 6400 \| 0.6782 \| 0.8417 \| 0.8418 \| \| 0.0687 \| 169.23 \| 6600 \| 0.6762 \| 0.8434 \| 0.8434 \| \| 0.0611 \| 174.36 \| 6800 \| 0.6971 \| 0.8368 \| 0.8369 \| \| 0.0628 \| 179.49 \| 7000 \| 0.7038 \| 0.8352 \| 0.8352 \| \| 0.0577 \| 184.62 \| 7200 \| 0.6977 \| 0.8368 \| 0.8369 \| \| 0.0569 \| 189.74 \| 7400 \| 0.6989 \| 0.8450 \| 0.8450 \| \| 0.0579 \| 194.87 \| 7600 \| 0.6972 \| 0.8450 \| 0.8450 \| \| 0.0572 \| 200.0 \| 7800 \| 0.7021 \| 0.8416 \| 0.8418 \| \| 0.0567 \| 205.13 \| 8000 \| 0.7044 \| 0.8320 \| 0.8320 \| \| 0.0549 \| 210.26 \| 8200 \| 0.7075 \| 0.8433 \| 0.8434 \| \| 0.0493 \| 215.38 \| 8400 \| 0.7109 \| 0.8369 \| 0.8369 \| \| 0.0514 \| 220.51 \| 8600 \| 0.7240 \| 0.8336 \| 0.8336 \| \| 0.0511 \| 225.64 \| 8800 \| 0.7316 \| 0.8401 \| 0.8401 \| \| 0.05 \| 230.77 \| 9000 \| 0.7390 \| 0.8418 \| 0.8418 \| \| 0.0501 \| 235.9 \| 9200 \| 0.7306 \| 0.8385 \| 0.8385 \| \| 0.0506 \| 241.03 \| 9400 \| 0.7358 \| 0.8401 \| 0.8401 \| \| 0.0482 \| 246.15 \| 9600 \| 0.7364 \| 0.8418 \| 0.8418 \| \| 0.0464 \| 251.28 \| 9800 \| 0.7357 \| 0.8401 \| 0.8401 \| \| 0.0482 \| 256.41 \| 10000 \| 0.7352 \| 0.8434 \| 0.8434 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_prom_prom_core_tata-seqsight_4096_512_46M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_prom_prom_core_tata-seqsight_4096_512_46M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_46M", "region:us" ]	null	2024-04-26T19:58:19+00:00	[]	[]	TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us	GUE\_prom\_prom\_core\_tata-seqsight\_4096\_512\_46M-L8\_f ========================================================== This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_46M on the mahdibaghbanzadeh/GUE\_prom\_prom\_core\_tata dataset. It achieves the following results on the evaluation set: * Loss: 0.6392 * F1 Score: 0.8303 * Accuracy: 0.8303 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]	[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]
text-generation	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 0.001_4iters_bs256_nodpo_only4w_userresponse_iter_1 This model is a fine-tuned version of [HuggingFaceH4/mistral-7b-sft-beta](https://huggingface.co/HuggingFaceH4/mistral-7b-sft-beta) on the updated and the original datasets. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.40.0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.19.1	{"license": "mit", "tags": ["alignment-handbook", "trl", "dpo", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["updated", "original"], "base_model": "HuggingFaceH4/mistral-7b-sft-beta", "model-index": [{"name": "0.001_4iters_bs256_nodpo_only4w_userresponse_iter_1", "results": []}]}	ShenaoZhang/0.001_4iters_bs256_nodpo_only4w_userresponse_iter_1	null	[ "transformers", "safetensors", "mistral", "text-generation", "alignment-handbook", "trl", "dpo", "generated_from_trainer", "conversational", "dataset:updated", "dataset:original", "base_model:HuggingFaceH4/mistral-7b-sft-beta", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-26T19:58:48+00:00	[]	[]	TAGS #transformers #safetensors #mistral #text-generation #alignment-handbook #trl #dpo #generated_from_trainer #conversational #dataset-updated #dataset-original #base_model-HuggingFaceH4/mistral-7b-sft-beta #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# 0.001_4iters_bs256_nodpo_only4w_userresponse_iter_1 This model is a fine-tuned version of HuggingFaceH4/mistral-7b-sft-beta on the updated and the original datasets. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.40.0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.19.1	[ "# 0.001_4iters_bs256_nodpo_only4w_userresponse_iter_1\n\nThis model is a fine-tuned version of HuggingFaceH4/mistral-7b-sft-beta on the updated and the original datasets.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 256\n- total_eval_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.40.0\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.19.1" ]	[ "TAGS\n#transformers #safetensors #mistral #text-generation #alignment-handbook #trl #dpo #generated_from_trainer #conversational #dataset-updated #dataset-original #base_model-HuggingFaceH4/mistral-7b-sft-beta #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# 0.001_4iters_bs256_nodpo_only4w_userresponse_iter_1\n\nThis model is a fine-tuned version of HuggingFaceH4/mistral-7b-sft-beta on the updated and the original datasets.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 256\n- total_eval_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.40.0\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.19.1" ]
null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/rimy/huggingface/runs/a0v6bf61) # results This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.1-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.41.0.dev0 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.19.1	{"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "TheBloke/Mistral-7B-Instruct-v0.1-GPTQ", "model-index": [{"name": "results", "results": []}]}	Rimyy/results	null	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ", "license:apache-2.0", "region:us" ]	null	2024-04-26T19:59:51+00:00	[]	[]	TAGS #peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-TheBloke/Mistral-7B-Instruct-v0.1-GPTQ #license-apache-2.0 #region-us	<img src="URL alt="Visualize in Weights & Biases" width="200" height="32"/> # results This model is a fine-tuned version of TheBloke/Mistral-7B-Instruct-v0.1-GPTQ on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.41.0.dev0 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.19.1	[ "# results\n\nThis model is a fine-tuned version of TheBloke/Mistral-7B-Instruct-v0.1-GPTQ on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- PEFT 0.10.0\n- Transformers 4.41.0.dev0\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.19.1" ]	[ "TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-TheBloke/Mistral-7B-Instruct-v0.1-GPTQ #license-apache-2.0 #region-us \n", "# results\n\nThis model is a fine-tuned version of TheBloke/Mistral-7B-Instruct-v0.1-GPTQ on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- PEFT 0.10.0\n- Transformers 4.41.0.dev0\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.19.1" ]
null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # GUE_prom_prom_core_tata-seqsight_4096_512_46M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_tata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_tata) dataset. It achieves the following results on the evaluation set: - Loss: 0.8199 - F1 Score: 0.8597 - Accuracy: 0.8597 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.5372 \| 5.13 \| 200 \| 0.5163 \| 0.7428 \| 0.7439 \| \| 0.4183 \| 10.26 \| 400 \| 0.4135 \| 0.8167 \| 0.8173 \| \| 0.3363 \| 15.38 \| 600 \| 0.3974 \| 0.8319 \| 0.8320 \| \| 0.2859 \| 20.51 \| 800 \| 0.3957 \| 0.8416 \| 0.8418 \| \| 0.2401 \| 25.64 \| 1000 \| 0.4302 \| 0.8189 \| 0.8189 \| \| 0.2023 \| 30.77 \| 1200 \| 0.4588 \| 0.8544 \| 0.8548 \| \| 0.1651 \| 35.9 \| 1400 \| 0.4583 \| 0.8450 \| 0.8450 \| \| 0.1387 \| 41.03 \| 1600 \| 0.5110 \| 0.8448 \| 0.8450 \| \| 0.1213 \| 46.15 \| 1800 \| 0.5007 \| 0.8434 \| 0.8434 \| \| 0.0993 \| 51.28 \| 2000 \| 0.5576 \| 0.8450 \| 0.8450 \| \| 0.0873 \| 56.41 \| 2200 \| 0.5942 \| 0.8531 \| 0.8532 \| \| 0.0796 \| 61.54 \| 2400 \| 0.6050 \| 0.8401 \| 0.8401 \| \| 0.0712 \| 66.67 \| 2600 \| 0.5897 \| 0.8463 \| 0.8467 \| \| 0.0625 \| 71.79 \| 2800 \| 0.6404 \| 0.8466 \| 0.8467 \| \| 0.0565 \| 76.92 \| 3000 \| 0.6529 \| 0.8434 \| 0.8434 \| \| 0.0515 \| 82.05 \| 3200 \| 0.7046 \| 0.8499 \| 0.8499 \| \| 0.0491 \| 87.18 \| 3400 \| 0.6990 \| 0.8515 \| 0.8515 \| \| 0.0458 \| 92.31 \| 3600 \| 0.7129 \| 0.8499 \| 0.8499 \| \| 0.0414 \| 97.44 \| 3800 \| 0.7288 \| 0.8482 \| 0.8483 \| \| 0.0401 \| 102.56 \| 4000 \| 0.6625 \| 0.8401 \| 0.8401 \| \| 0.0372 \| 107.69 \| 4200 \| 0.6802 \| 0.8466 \| 0.8467 \| \| 0.0331 \| 112.82 \| 4400 \| 0.7559 \| 0.8548 \| 0.8548 \| \| 0.0327 \| 117.95 \| 4600 \| 0.7604 \| 0.8385 \| 0.8385 \| \| 0.0325 \| 123.08 \| 4800 \| 0.7189 \| 0.8483 \| 0.8483 \| \| 0.0289 \| 128.21 \| 5000 \| 0.7833 \| 0.8483 \| 0.8483 \| \| 0.0292 \| 133.33 \| 5200 \| 0.7775 \| 0.8515 \| 0.8515 \| \| 0.0294 \| 138.46 \| 5400 \| 0.7406 \| 0.8597 \| 0.8597 \| \| 0.0246 \| 143.59 \| 5600 \| 0.7963 \| 0.8548 \| 0.8548 \| \| 0.0256 \| 148.72 \| 5800 \| 0.7805 \| 0.8482 \| 0.8483 \| \| 0.0249 \| 153.85 \| 6000 \| 0.8132 \| 0.8613 \| 0.8613 \| \| 0.0248 \| 158.97 \| 6200 \| 0.7969 \| 0.8531 \| 0.8532 \| \| 0.0223 \| 164.1 \| 6400 \| 0.7997 \| 0.8532 \| 0.8532 \| \| 0.0219 \| 169.23 \| 6600 \| 0.8132 \| 0.8434 \| 0.8434 \| \| 0.0181 \| 174.36 \| 6800 \| 0.8788 \| 0.8483 \| 0.8483 \| \| 0.0205 \| 179.49 \| 7000 \| 0.8876 \| 0.8450 \| 0.8450 \| \| 0.0184 \| 184.62 \| 7200 \| 0.8420 \| 0.8450 \| 0.8450 \| \| 0.0181 \| 189.74 \| 7400 \| 0.8651 \| 0.8548 \| 0.8548 \| \| 0.018 \| 194.87 \| 7600 \| 0.8124 \| 0.8564 \| 0.8564 \| \| 0.018 \| 200.0 \| 7800 \| 0.8607 \| 0.8499 \| 0.8499 \| \| 0.0183 \| 205.13 \| 8000 \| 0.8451 \| 0.8548 \| 0.8548 \| \| 0.0169 \| 210.26 \| 8200 \| 0.8448 \| 0.8434 \| 0.8434 \| \| 0.0157 \| 215.38 \| 8400 \| 0.8915 \| 0.8434 \| 0.8434 \| \| 0.0152 \| 220.51 \| 8600 \| 0.9034 \| 0.8450 \| 0.8450 \| \| 0.0151 \| 225.64 \| 8800 \| 0.9155 \| 0.8483 \| 0.8483 \| \| 0.0159 \| 230.77 \| 9000 \| 0.8968 \| 0.8515 \| 0.8515 \| \| 0.0136 \| 235.9 \| 9200 \| 0.9146 \| 0.8434 \| 0.8434 \| \| 0.0148 \| 241.03 \| 9400 \| 0.8961 \| 0.8467 \| 0.8467 \| \| 0.0134 \| 246.15 \| 9600 \| 0.9116 \| 0.8483 \| 0.8483 \| \| 0.0149 \| 251.28 \| 9800 \| 0.9067 \| 0.8499 \| 0.8499 \| \| 0.0133 \| 256.41 \| 10000 \| 0.9077 \| 0.8532 \| 0.8532 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_prom_prom_core_tata-seqsight_4096_512_46M-L32_f", "results": []}]}	mahdibaghbanzadeh/GUE_prom_prom_core_tata-seqsight_4096_512_46M-L32_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_46M", "region:us" ]	null	2024-04-26T20:00:34+00:00	[]	[]	TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us	GUE\_prom\_prom\_core\_tata-seqsight\_4096\_512\_46M-L32\_f =========================================================== This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_46M on the mahdibaghbanzadeh/GUE\_prom\_prom\_core\_tata dataset. It achieves the following results on the evaluation set: * Loss: 0.8199 * F1 Score: 0.8597 * Accuracy: 0.8597 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]	[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]
text2text-generation	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_opus_books_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.1944 - Bleu: 0.1991 - Gen Len: 18.18 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\| \| 3.6446 \| 1.0 \| 1617 \| 3.2778 \| 0.1513 \| 18.2069 \| \| 3.5134 \| 2.0 \| 3234 \| 3.1944 \| 0.1991 \| 18.18 \| ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "base_model": "t5-small", "model-index": [{"name": "my_awesome_opus_books_model", "results": []}]}	BohanJiang/my_awesome_opus_books_model	null	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:t5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-26T20:00:55+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-t5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	my\_awesome\_opus\_books\_model =============================== This model is a fine-tuned version of t5-small on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 3.1944 * Bleu: 0.1991 * Gen Len: 18.18 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 2 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.40.0 * Pytorch 2.2.1+cu121 * Datasets 2.19.0 * Tokenizers 0.19.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-t5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]
null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # GUE_prom_prom_300_all-seqsight_4096_512_46M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_all](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_all) dataset. It achieves the following results on the evaluation set: - Loss: 0.2014 - F1 Score: 0.9186 - Accuracy: 0.9186 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.3473 \| 0.54 \| 200 \| 0.2737 \| 0.8912 \| 0.8912 \| \| 0.2744 \| 1.08 \| 400 \| 0.2439 \| 0.9019 \| 0.9019 \| \| 0.2531 \| 1.62 \| 600 \| 0.2441 \| 0.9004 \| 0.9005 \| \| 0.2354 \| 2.16 \| 800 \| 0.2235 \| 0.9095 \| 0.9095 \| \| 0.2294 \| 2.7 \| 1000 \| 0.2301 \| 0.9069 \| 0.9069 \| \| 0.2212 \| 3.24 \| 1200 \| 0.2134 \| 0.9135 \| 0.9135 \| \| 0.2206 \| 3.78 \| 1400 \| 0.2116 \| 0.9144 \| 0.9144 \| \| 0.2135 \| 4.32 \| 1600 \| 0.2096 \| 0.9128 \| 0.9128 \| \| 0.2055 \| 4.86 \| 1800 \| 0.2105 \| 0.9181 \| 0.9181 \| \| 0.2048 \| 5.41 \| 2000 \| 0.2123 \| 0.9160 \| 0.9160 \| \| 0.2031 \| 5.95 \| 2200 \| 0.2002 \| 0.9204 \| 0.9204 \| \| 0.1966 \| 6.49 \| 2400 \| 0.2069 \| 0.9162 \| 0.9162 \| \| 0.2001 \| 7.03 \| 2600 \| 0.2018 \| 0.9211 \| 0.9211 \| \| 0.192 \| 7.57 \| 2800 \| 0.2009 \| 0.9163 \| 0.9164 \| \| 0.1942 \| 8.11 \| 3000 \| 0.2094 \| 0.9165 \| 0.9166 \| \| 0.1921 \| 8.65 \| 3200 \| 0.1999 \| 0.9189 \| 0.9189 \| \| 0.186 \| 9.19 \| 3400 \| 0.2058 \| 0.9172 \| 0.9172 \| \| 0.1859 \| 9.73 \| 3600 \| 0.1935 \| 0.9216 \| 0.9216 \| \| 0.1944 \| 10.27 \| 3800 \| 0.1906 \| 0.9236 \| 0.9236 \| \| 0.1856 \| 10.81 \| 4000 \| 0.1931 \| 0.9216 \| 0.9216 \| \| 0.1823 \| 11.35 \| 4200 \| 0.1926 \| 0.9241 \| 0.9242 \| \| 0.1809 \| 11.89 \| 4400 \| 0.1965 \| 0.9243 \| 0.9243 \| \| 0.181 \| 12.43 \| 4600 \| 0.1884 \| 0.9246 \| 0.9247 \| \| 0.1852 \| 12.97 \| 4800 \| 0.1896 \| 0.9231 \| 0.9231 \| \| 0.1781 \| 13.51 \| 5000 \| 0.1869 \| 0.9240 \| 0.9240 \| \| 0.1809 \| 14.05 \| 5200 \| 0.1890 \| 0.9250 \| 0.925 \| \| 0.1774 \| 14.59 \| 5400 \| 0.1824 \| 0.9274 \| 0.9274 \| \| 0.175 \| 15.14 \| 5600 \| 0.1836 \| 0.9277 \| 0.9277 \| \| 0.1751 \| 15.68 \| 5800 \| 0.1880 \| 0.9234 \| 0.9235 \| \| 0.1772 \| 16.22 \| 6000 \| 0.1806 \| 0.9289 \| 0.9289 \| \| 0.1753 \| 16.76 \| 6200 \| 0.1841 \| 0.9272 \| 0.9272 \| \| 0.1751 \| 17.3 \| 6400 \| 0.1916 \| 0.9227 \| 0.9228 \| \| 0.1756 \| 17.84 \| 6600 \| 0.1861 \| 0.9248 \| 0.9248 \| \| 0.1682 \| 18.38 \| 6800 \| 0.1863 \| 0.9256 \| 0.9257 \| \| 0.1768 \| 18.92 \| 7000 \| 0.1835 \| 0.9253 \| 0.9253 \| \| 0.1723 \| 19.46 \| 7200 \| 0.1886 \| 0.9260 \| 0.9260 \| \| 0.1714 \| 20.0 \| 7400 \| 0.1823 \| 0.9279 \| 0.9279 \| \| 0.1713 \| 20.54 \| 7600 \| 0.1845 \| 0.9268 \| 0.9269 \| \| 0.169 \| 21.08 \| 7800 \| 0.1821 \| 0.9275 \| 0.9275 \| \| 0.1711 \| 21.62 \| 8000 \| 0.1827 \| 0.9267 \| 0.9267 \| \| 0.169 \| 22.16 \| 8200 \| 0.1834 \| 0.9282 \| 0.9282 \| \| 0.1667 \| 22.7 \| 8400 \| 0.1854 \| 0.9267 \| 0.9267 \| \| 0.1698 \| 23.24 \| 8600 \| 0.1871 \| 0.9267 \| 0.9267 \| \| 0.1669 \| 23.78 \| 8800 \| 0.1821 \| 0.9272 \| 0.9272 \| \| 0.1697 \| 24.32 \| 9000 \| 0.1872 \| 0.9260 \| 0.9260 \| \| 0.1653 \| 24.86 \| 9200 \| 0.1853 \| 0.9268 \| 0.9269 \| \| 0.1681 \| 25.41 \| 9400 \| 0.1871 \| 0.9263 \| 0.9264 \| \| 0.1668 \| 25.95 \| 9600 \| 0.1848 \| 0.9280 \| 0.9280 \| \| 0.1661 \| 26.49 \| 9800 \| 0.1822 \| 0.9279 \| 0.9279 \| \| 0.1645 \| 27.03 \| 10000 \| 0.1839 \| 0.9275 \| 0.9275 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_prom_prom_300_all-seqsight_4096_512_46M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_prom_prom_300_all-seqsight_4096_512_46M-L1_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_46M", "region:us" ]	null	2024-04-26T20:04:02+00:00	[]	[]	TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us	GUE\_prom\_prom\_300\_all-seqsight\_4096\_512\_46M-L1\_f ======================================================== This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_46M on the mahdibaghbanzadeh/GUE\_prom\_prom\_300\_all dataset. It achieves the following results on the evaluation set: * Loss: 0.2014 * F1 Score: 0.9186 * Accuracy: 0.9186 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]	[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]
text-generation	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # MentalRoberta-GPT2_EmpAI_EOSbajo This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2970 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 8000 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:-----:\|:---------------:\| \| 0.3476 \| 0.9999 \| 5998 \| 0.3400 \| \| 0.2941 \| 2.0 \| 11997 \| 0.3041 \| \| 0.2917 \| 2.9997 \| 17994 \| 0.2970 \| ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "openai-community/gpt2", "model-index": [{"name": "MentalRoberta-GPT2_EmpAI_EOSbajo", "results": []}]}	LuangMV97/MentalRoberta-GPT2_EmpAI_EOSbajo	null	[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:openai-community/gpt2", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-26T20:05:09+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-openai-community/gpt2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	MentalRoberta-GPT2\_EmpAI\_EOSbajo ================================== This model is a fine-tuned version of openai-community/gpt2 on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.2970 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 1e-05 * train\_batch\_size: 4 * eval\_batch\_size: 4 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 8 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 8000 * num\_epochs: 3 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.40.0 * Pytorch 2.2.1+cu121 * Datasets 2.19.0 * Tokenizers 0.19.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 8000\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-openai-community/gpt2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 8000\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	pruning/w6doc6r	null	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-26T20:06:24+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #stablelm #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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(2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #stablelm #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	pruning/umk17z9	null	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-26T20:06:24+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #stablelm #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #stablelm #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	pruning/kgk4u66	null	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-26T20:06:24+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #stablelm #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #stablelm #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	pruning/tn3hfw0	null	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-26T20:06:24+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #stablelm #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #stablelm #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	pruning/9m47dti	null	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-26T20:06:25+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #stablelm #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #stablelm #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	pruning/dj4d7mu	null	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-26T20:06:25+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #stablelm #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #stablelm #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # code-llama-7b-text-to-sql This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 3 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 6 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.7.2.dev0 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.2	{"license": "llama2", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "codellama/CodeLlama-7b-hf", "model-index": [{"name": "code-llama-7b-text-to-sql", "results": []}]}	nan-hu-earnin/code-llama-7b-text-to-sql	null	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:codellama/CodeLlama-7b-hf", "license:llama2", "region:us" ]	null	2024-04-26T20:07:06+00:00	[]	[]	TAGS #peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-codellama/CodeLlama-7b-hf #license-llama2 #region-us	# code-llama-7b-text-to-sql This model is a fine-tuned version of codellama/CodeLlama-7b-hf on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 3 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 6 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.7.2.dev0 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.2	[ "# code-llama-7b-text-to-sql\n\nThis model is a fine-tuned version of codellama/CodeLlama-7b-hf on the generator dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 3\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 6\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 3", "### Training results", "### Framework versions\n\n- PEFT 0.7.2.dev0\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.2" ]	[ "TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-codellama/CodeLlama-7b-hf #license-llama2 #region-us \n", "# code-llama-7b-text-to-sql\n\nThis model is a fine-tuned version of codellama/CodeLlama-7b-hf on the generator dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 3\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 6\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 3", "### Training results", "### Framework versions\n\n- PEFT 0.7.2.dev0\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.2" ]
text-generation	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_new_lm This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the eli5_category dataset. It achieves the following results on the evaluation set: - Loss: 3.5791 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.6897 \| 1.0 \| 1288 \| 3.5826 \| \| 3.5716 \| 2.0 \| 2576 \| 3.5785 \| \| 3.5233 \| 3.0 \| 3864 \| 3.5791 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["eli5_category"], "base_model": "gpt2", "model-index": [{"name": "my_awesome_new_lm", "results": []}]}	ljgries/my_awesome_new_lm	null	[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "dataset:eli5_category", "base_model:gpt2", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-26T20:07:55+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #dataset-eli5_category #base_model-gpt2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	my\_awesome\_new\_lm ==================== This model is a fine-tuned version of gpt2 on the eli5\_category dataset. It achieves the following results on the evaluation set: * Loss: 3.5791 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3.0 ### Training results ### Framework versions * Transformers 4.40.1 * Pytorch 2.2.1+cu121 * Datasets 2.19.0 * Tokenizers 0.19.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.1\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #dataset-eli5_category #base_model-gpt2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.1\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]
text-generation	transformers	<div align="center"> # TinyLlama-1.1B </div> We used this version of TinyLlama as a base model: https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0 The goal was to improve performance on basic algebra (i.e. solving systems of linear equations). The base model was fine tuned on 8k rows synthetic solution data generated by [OpenMath-Mistral-7B-v0.1-hf](https://huggingface.co/nvidia/OpenMath-Mistral-7B-v0.1-hf) on [ALG-514](https://paperswithcode.com/sota/math-word-problem-solving-on-alg514). We used the [NeMo Skills](https://github.com/Kipok/NeMo-Skills) pipeline for inference with code execution and generating the synthetic data. HuggingFace's SFTTrainer was used for fine tuning, as the NeMo Skills pipeline is a buggy mess. It took 30 minutes to fine tune on an RTX3090. Notes from previous model cards: > We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint. #### Eval ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64388bdd43d932c4623e4983/H07dGzwOfzcvP1GFA1GUq.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64388bdd43d932c4623e4983/Qr7rvIms3AL67jltHBXnr.png) Note that `checkpoint_0` is the base model and `checkpoint_mistral` is OpenMath-Mistral-7B-v0.1-hf. The performance is _not good_™, but this model could be used to quickly generate synthetic data, as the coverage is decent for this dataset. The uploaded model is checkpoint-2.6k. People involved in creating this fine tune: - Coulton Theuer [[email protected]] - Bret Ellenbogen [[email protected]] - Victoria Chang [[email protected]]	{"language": ["en"], "license": "apache-2.0"}	theuerc/TinyLlama-1.1B-Chat-Math-v1.0	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-26T20:08:33+00:00	[]	[ "en" ]	TAGS #transformers #safetensors #llama #text-generation #conversational #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	<div align="center"> # TinyLlama-1.1B </div> We used this version of TinyLlama as a base model: URL The goal was to improve performance on basic algebra (i.e. solving systems of linear equations). The base model was fine tuned on 8k rows synthetic solution data generated by OpenMath-Mistral-7B-v0.1-hf on ALG-514. We used the NeMo Skills pipeline for inference with code execution and generating the synthetic data. HuggingFace's SFTTrainer was used for fine tuning, as the NeMo Skills pipeline is a buggy mess. It took 30 minutes to fine tune on an RTX3090. Notes from previous model cards: > We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint. #### Eval !image/png !image/png Note that 'checkpoint_0' is the base model and 'checkpoint_mistral' is OpenMath-Mistral-7B-v0.1-hf. The performance is _not good_™, but this model could be used to quickly generate synthetic data, as the coverage is decent for this dataset. The uploaded model is checkpoint-2.6k. People involved in creating this fine tune: - Coulton Theuer [theuerc@URL] - Bret Ellenbogen [bretelle@URL] - Victoria Chang [vgc@URL]	[ "# TinyLlama-1.1B\n</div>\n\nWe used this version of TinyLlama as a base model:\nURL\n\nThe goal was to improve performance on basic algebra (i.e. solving systems of linear equations).\n\nThe base model was fine tuned on 8k rows synthetic solution data generated by OpenMath-Mistral-7B-v0.1-hf on ALG-514.\n\nWe used the NeMo Skills pipeline for inference with code execution and generating the synthetic data. HuggingFace's SFTTrainer was used for fine tuning, as the NeMo Skills pipeline is a buggy mess. It took 30 minutes to fine tune on an RTX3090.\n\n\nNotes from previous model cards:\n> We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.", "#### Eval\n\n!image/png\n\n!image/png\n\nNote that 'checkpoint_0' is the base model and 'checkpoint_mistral' is OpenMath-Mistral-7B-v0.1-hf.\n\nThe performance is _not good_™, but this model could be used to quickly generate synthetic data, as the coverage is decent for this dataset. The uploaded model is checkpoint-2.6k.\n\n\nPeople involved in creating this fine tune: \n- Coulton Theuer [theuerc@URL]\n- Bret Ellenbogen [bretelle@URL]\n- Victoria Chang [vgc@URL]" ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #conversational #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# TinyLlama-1.1B\n</div>\n\nWe used this version of TinyLlama as a base model:\nURL\n\nThe goal was to improve performance on basic algebra (i.e. solving systems of linear equations).\n\nThe base model was fine tuned on 8k rows synthetic solution data generated by OpenMath-Mistral-7B-v0.1-hf on ALG-514.\n\nWe used the NeMo Skills pipeline for inference with code execution and generating the synthetic data. HuggingFace's SFTTrainer was used for fine tuning, as the NeMo Skills pipeline is a buggy mess. It took 30 minutes to fine tune on an RTX3090.\n\n\nNotes from previous model cards:\n> We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.", "#### Eval\n\n!image/png\n\n!image/png\n\nNote that 'checkpoint_0' is the base model and 'checkpoint_mistral' is OpenMath-Mistral-7B-v0.1-hf.\n\nThe performance is _not good_™, but this model could be used to quickly generate synthetic data, as the coverage is decent for this dataset. The uploaded model is checkpoint-2.6k.\n\n\nPeople involved in creating this fine tune: \n- Coulton Theuer [theuerc@URL]\n- Bret Ellenbogen [bretelle@URL]\n- Victoria Chang [vgc@URL]" ]
null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # GUE_prom_prom_300_all-seqsight_4096_512_46M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_all](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_all) dataset. It achieves the following results on the evaluation set: - Loss: 0.1975 - F1 Score: 0.9246 - Accuracy: 0.9247 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.3196 \| 0.54 \| 200 \| 0.2469 \| 0.9029 \| 0.9029 \| \| 0.2439 \| 1.08 \| 400 \| 0.2205 \| 0.9132 \| 0.9132 \| \| 0.2294 \| 1.62 \| 600 \| 0.2277 \| 0.9057 \| 0.9057 \| \| 0.2141 \| 2.16 \| 800 \| 0.2027 \| 0.9209 \| 0.9209 \| \| 0.2083 \| 2.7 \| 1000 \| 0.2019 \| 0.9179 \| 0.9179 \| \| 0.2013 \| 3.24 \| 1200 \| 0.1946 \| 0.9221 \| 0.9221 \| \| 0.1984 \| 3.78 \| 1400 \| 0.1923 \| 0.9223 \| 0.9223 \| \| 0.194 \| 4.32 \| 1600 \| 0.1892 \| 0.9240 \| 0.9240 \| \| 0.1855 \| 4.86 \| 1800 \| 0.2015 \| 0.9202 \| 0.9203 \| \| 0.1829 \| 5.41 \| 2000 \| 0.2014 \| 0.9214 \| 0.9215 \| \| 0.1822 \| 5.95 \| 2200 \| 0.1839 \| 0.9243 \| 0.9243 \| \| 0.175 \| 6.49 \| 2400 \| 0.1885 \| 0.9207 \| 0.9208 \| \| 0.1776 \| 7.03 \| 2600 \| 0.1802 \| 0.9279 \| 0.9279 \| \| 0.1678 \| 7.57 \| 2800 \| 0.1878 \| 0.9214 \| 0.9215 \| \| 0.1716 \| 8.11 \| 3000 \| 0.1953 \| 0.9190 \| 0.9191 \| \| 0.1665 \| 8.65 \| 3200 \| 0.1772 \| 0.9233 \| 0.9233 \| \| 0.1611 \| 9.19 \| 3400 \| 0.1872 \| 0.9239 \| 0.9240 \| \| 0.1603 \| 9.73 \| 3600 \| 0.1788 \| 0.9273 \| 0.9274 \| \| 0.1684 \| 10.27 \| 3800 \| 0.1743 \| 0.9284 \| 0.9284 \| \| 0.1605 \| 10.81 \| 4000 \| 0.1840 \| 0.9246 \| 0.9247 \| \| 0.1575 \| 11.35 \| 4200 \| 0.1830 \| 0.9273 \| 0.9274 \| \| 0.155 \| 11.89 \| 4400 \| 0.1807 \| 0.9265 \| 0.9265 \| \| 0.153 \| 12.43 \| 4600 \| 0.1738 \| 0.9295 \| 0.9296 \| \| 0.1549 \| 12.97 \| 4800 \| 0.1693 \| 0.9309 \| 0.9309 \| \| 0.1497 \| 13.51 \| 5000 \| 0.1735 \| 0.9295 \| 0.9296 \| \| 0.1495 \| 14.05 \| 5200 \| 0.1743 \| 0.9299 \| 0.9299 \| \| 0.1476 \| 14.59 \| 5400 \| 0.1701 \| 0.9324 \| 0.9324 \| \| 0.1461 \| 15.14 \| 5600 \| 0.1743 \| 0.9307 \| 0.9307 \| \| 0.1451 \| 15.68 \| 5800 \| 0.1791 \| 0.9280 \| 0.9280 \| \| 0.1456 \| 16.22 \| 6000 \| 0.1684 \| 0.9326 \| 0.9326 \| \| 0.1436 \| 16.76 \| 6200 \| 0.1755 \| 0.9314 \| 0.9314 \| \| 0.1431 \| 17.3 \| 6400 \| 0.1776 \| 0.9297 \| 0.9297 \| \| 0.1452 \| 17.84 \| 6600 \| 0.1743 \| 0.9312 \| 0.9313 \| \| 0.1373 \| 18.38 \| 6800 \| 0.1736 \| 0.9317 \| 0.9318 \| \| 0.1419 \| 18.92 \| 7000 \| 0.1694 \| 0.9334 \| 0.9334 \| \| 0.1397 \| 19.46 \| 7200 \| 0.1785 \| 0.9304 \| 0.9304 \| \| 0.1366 \| 20.0 \| 7400 \| 0.1709 \| 0.9326 \| 0.9326 \| \| 0.1362 \| 20.54 \| 7600 \| 0.1760 \| 0.9314 \| 0.9314 \| \| 0.1362 \| 21.08 \| 7800 \| 0.1705 \| 0.9333 \| 0.9333 \| \| 0.1351 \| 21.62 \| 8000 \| 0.1751 \| 0.9324 \| 0.9324 \| \| 0.1337 \| 22.16 \| 8200 \| 0.1726 \| 0.9317 \| 0.9318 \| \| 0.1303 \| 22.7 \| 8400 \| 0.1738 \| 0.9317 \| 0.9318 \| \| 0.1305 \| 23.24 \| 8600 \| 0.1715 \| 0.9341 \| 0.9341 \| \| 0.1323 \| 23.78 \| 8800 \| 0.1717 \| 0.9329 \| 0.9329 \| \| 0.1312 \| 24.32 \| 9000 \| 0.1781 \| 0.9305 \| 0.9306 \| \| 0.1328 \| 24.86 \| 9200 \| 0.1716 \| 0.9339 \| 0.9340 \| \| 0.1294 \| 25.41 \| 9400 \| 0.1773 \| 0.9305 \| 0.9306 \| \| 0.1321 \| 25.95 \| 9600 \| 0.1749 \| 0.9317 \| 0.9318 \| \| 0.1293 \| 26.49 \| 9800 \| 0.1729 \| 0.9327 \| 0.9328 \| \| 0.1284 \| 27.03 \| 10000 \| 0.1738 \| 0.9321 \| 0.9321 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_prom_prom_300_all-seqsight_4096_512_46M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_prom_prom_300_all-seqsight_4096_512_46M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_46M", "region:us" ]	null	2024-04-26T20:08:57+00:00	[]	[]	TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us	GUE\_prom\_prom\_300\_all-seqsight\_4096\_512\_46M-L8\_f ======================================================== This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_46M on the mahdibaghbanzadeh/GUE\_prom\_prom\_300\_all dataset. It achieves the following results on the evaluation set: * Loss: 0.1975 * F1 Score: 0.9246 * Accuracy: 0.9247 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]	[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]
null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # GUE_prom_prom_300_all-seqsight_4096_512_46M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_all](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_all) dataset. It achieves the following results on the evaluation set: - Loss: 0.1915 - F1 Score: 0.9236 - Accuracy: 0.9236 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.2961 \| 0.54 \| 200 \| 0.2382 \| 0.9083 \| 0.9083 \| \| 0.2282 \| 1.08 \| 400 \| 0.2118 \| 0.9167 \| 0.9167 \| \| 0.2166 \| 1.62 \| 600 \| 0.2036 \| 0.9191 \| 0.9191 \| \| 0.1989 \| 2.16 \| 800 \| 0.1856 \| 0.9255 \| 0.9255 \| \| 0.1936 \| 2.7 \| 1000 \| 0.1904 \| 0.9202 \| 0.9203 \| \| 0.1846 \| 3.24 \| 1200 \| 0.1763 \| 0.9304 \| 0.9304 \| \| 0.1803 \| 3.78 \| 1400 \| 0.1784 \| 0.9246 \| 0.9247 \| \| 0.1757 \| 4.32 \| 1600 \| 0.1797 \| 0.9268 \| 0.9269 \| \| 0.1685 \| 4.86 \| 1800 \| 0.1864 \| 0.9212 \| 0.9213 \| \| 0.1645 \| 5.41 \| 2000 \| 0.1825 \| 0.9244 \| 0.9245 \| \| 0.1649 \| 5.95 \| 2200 \| 0.1713 \| 0.9287 \| 0.9287 \| \| 0.1554 \| 6.49 \| 2400 \| 0.1837 \| 0.9203 \| 0.9204 \| \| 0.1596 \| 7.03 \| 2600 \| 0.1654 \| 0.9326 \| 0.9326 \| \| 0.149 \| 7.57 \| 2800 \| 0.1679 \| 0.9283 \| 0.9284 \| \| 0.1494 \| 8.11 \| 3000 \| 0.1779 \| 0.9294 \| 0.9294 \| \| 0.1467 \| 8.65 \| 3200 \| 0.1604 \| 0.9336 \| 0.9336 \| \| 0.1393 \| 9.19 \| 3400 \| 0.1757 \| 0.9266 \| 0.9267 \| \| 0.1376 \| 9.73 \| 3600 \| 0.1739 \| 0.9297 \| 0.9297 \| \| 0.1451 \| 10.27 \| 3800 \| 0.1667 \| 0.9304 \| 0.9304 \| \| 0.1377 \| 10.81 \| 4000 \| 0.1770 \| 0.9295 \| 0.9296 \| \| 0.1322 \| 11.35 \| 4200 \| 0.1680 \| 0.9324 \| 0.9324 \| \| 0.1312 \| 11.89 \| 4400 \| 0.1736 \| 0.9317 \| 0.9318 \| \| 0.1284 \| 12.43 \| 4600 \| 0.1709 \| 0.9322 \| 0.9323 \| \| 0.1284 \| 12.97 \| 4800 \| 0.1614 \| 0.9351 \| 0.9351 \| \| 0.1227 \| 13.51 \| 5000 \| 0.1781 \| 0.9285 \| 0.9285 \| \| 0.1231 \| 14.05 \| 5200 \| 0.1704 \| 0.9305 \| 0.9306 \| \| 0.1182 \| 14.59 \| 5400 \| 0.1663 \| 0.9341 \| 0.9341 \| \| 0.1185 \| 15.14 \| 5600 \| 0.1773 \| 0.9281 \| 0.9282 \| \| 0.1149 \| 15.68 \| 5800 \| 0.1893 \| 0.9298 \| 0.9299 \| \| 0.116 \| 16.22 \| 6000 \| 0.1670 \| 0.9350 \| 0.9350 \| \| 0.1114 \| 16.76 \| 6200 \| 0.1812 \| 0.9303 \| 0.9304 \| \| 0.1122 \| 17.3 \| 6400 \| 0.1755 \| 0.9303 \| 0.9304 \| \| 0.1115 \| 17.84 \| 6600 \| 0.1776 \| 0.9307 \| 0.9307 \| \| 0.1043 \| 18.38 \| 6800 \| 0.1797 \| 0.9329 \| 0.9329 \| \| 0.1069 \| 18.92 \| 7000 \| 0.1718 \| 0.9339 \| 0.9340 \| \| 0.1046 \| 19.46 \| 7200 \| 0.1826 \| 0.9319 \| 0.9319 \| \| 0.1016 \| 20.0 \| 7400 \| 0.1825 \| 0.9319 \| 0.9319 \| \| 0.1013 \| 20.54 \| 7600 \| 0.1754 \| 0.9331 \| 0.9331 \| \| 0.1019 \| 21.08 \| 7800 \| 0.1747 \| 0.9341 \| 0.9341 \| \| 0.0981 \| 21.62 \| 8000 \| 0.1815 \| 0.9322 \| 0.9323 \| \| 0.0977 \| 22.16 \| 8200 \| 0.1801 \| 0.9331 \| 0.9331 \| \| 0.0926 \| 22.7 \| 8400 \| 0.1866 \| 0.9326 \| 0.9326 \| \| 0.095 \| 23.24 \| 8600 \| 0.1792 \| 0.9350 \| 0.9350 \| \| 0.0926 \| 23.78 \| 8800 \| 0.1840 \| 0.9336 \| 0.9336 \| \| 0.0926 \| 24.32 \| 9000 \| 0.1880 \| 0.9324 \| 0.9324 \| \| 0.0911 \| 24.86 \| 9200 \| 0.1821 \| 0.9346 \| 0.9346 \| \| 0.088 \| 25.41 \| 9400 \| 0.1905 \| 0.9329 \| 0.9329 \| \| 0.0924 \| 25.95 \| 9600 \| 0.1874 \| 0.9321 \| 0.9321 \| \| 0.0899 \| 26.49 \| 9800 \| 0.1867 \| 0.9327 \| 0.9328 \| \| 0.0878 \| 27.03 \| 10000 \| 0.1868 \| 0.9322 \| 0.9323 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_prom_prom_300_all-seqsight_4096_512_46M-L32_f", "results": []}]}	mahdibaghbanzadeh/GUE_prom_prom_300_all-seqsight_4096_512_46M-L32_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_46M", "region:us" ]	null	2024-04-26T20:09:05+00:00	[]	[]	TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us	GUE\_prom\_prom\_300\_all-seqsight\_4096\_512\_46M-L32\_f ========================================================= This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_46M on the mahdibaghbanzadeh/GUE\_prom\_prom\_300\_all dataset. It achieves the following results on the evaluation set: * Loss: 0.1915 * F1 Score: 0.9236 * Accuracy: 0.9236 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]	[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]
null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # GUE_EMP_H3K14ac-seqsight_4096_512_46M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_EMP_H3K14ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K14ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.4728 - F1 Score: 0.7696 - Accuracy: 0.7691 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.5656 \| 0.97 \| 200 \| 0.5316 \| 0.7443 \| 0.7425 \| \| 0.524 \| 1.93 \| 400 \| 0.5002 \| 0.7667 \| 0.7655 \| \| 0.5078 \| 2.9 \| 600 \| 0.5143 \| 0.7621 \| 0.7607 \| \| 0.5036 \| 3.86 \| 800 \| 0.4868 \| 0.7737 \| 0.7737 \| \| 0.4995 \| 4.83 \| 1000 \| 0.5012 \| 0.7639 \| 0.7625 \| \| 0.4916 \| 5.8 \| 1200 \| 0.4945 \| 0.7695 \| 0.7679 \| \| 0.4904 \| 6.76 \| 1400 \| 0.4811 \| 0.7734 \| 0.7719 \| \| 0.4851 \| 7.73 \| 1600 \| 0.4808 \| 0.7789 \| 0.7776 \| \| 0.4825 \| 8.7 \| 1800 \| 0.4791 \| 0.7782 \| 0.7767 \| \| 0.4775 \| 9.66 \| 2000 \| 0.4805 \| 0.7776 \| 0.7761 \| \| 0.483 \| 10.63 \| 2200 \| 0.4928 \| 0.7712 \| 0.7697 \| \| 0.4717 \| 11.59 \| 2400 \| 0.4730 \| 0.7798 \| 0.7785 \| \| 0.4769 \| 12.56 \| 2600 \| 0.4842 \| 0.7779 \| 0.7764 \| \| 0.4716 \| 13.53 \| 2800 \| 0.4786 \| 0.7746 \| 0.7731 \| \| 0.4732 \| 14.49 \| 3000 \| 0.4813 \| 0.7731 \| 0.7716 \| \| 0.4646 \| 15.46 \| 3200 \| 0.4749 \| 0.7800 \| 0.7788 \| \| 0.4703 \| 16.43 \| 3400 \| 0.4835 \| 0.7749 \| 0.7734 \| \| 0.4664 \| 17.39 \| 3600 \| 0.4700 \| 0.7805 \| 0.7794 \| \| 0.4645 \| 18.36 \| 3800 \| 0.4715 \| 0.7784 \| 0.7785 \| \| 0.4633 \| 19.32 \| 4000 \| 0.4713 \| 0.7847 \| 0.7834 \| \| 0.4597 \| 20.29 \| 4200 \| 0.4761 \| 0.7812 \| 0.7800 \| \| 0.462 \| 21.26 \| 4400 \| 0.4740 \| 0.7863 \| 0.7849 \| \| 0.4596 \| 22.22 \| 4600 \| 0.4812 \| 0.7785 \| 0.7770 \| \| 0.4597 \| 23.19 \| 4800 \| 0.4693 \| 0.7873 \| 0.7861 \| \| 0.459 \| 24.15 \| 5000 \| 0.4714 \| 0.7849 \| 0.7837 \| \| 0.4567 \| 25.12 \| 5200 \| 0.4793 \| 0.7812 \| 0.7797 \| \| 0.4551 \| 26.09 \| 5400 \| 0.4692 \| 0.7857 \| 0.7843 \| \| 0.4562 \| 27.05 \| 5600 \| 0.4773 \| 0.7833 \| 0.7818 \| \| 0.4522 \| 28.02 \| 5800 \| 0.4689 \| 0.7874 \| 0.7864 \| \| 0.4536 \| 28.99 \| 6000 \| 0.4760 \| 0.7824 \| 0.7809 \| \| 0.4521 \| 29.95 \| 6200 \| 0.4757 \| 0.7851 \| 0.7837 \| \| 0.4519 \| 30.92 \| 6400 \| 0.4663 \| 0.7874 \| 0.7867 \| \| 0.4512 \| 31.88 \| 6600 \| 0.4705 \| 0.7877 \| 0.7864 \| \| 0.4482 \| 32.85 \| 6800 \| 0.4728 \| 0.7890 \| 0.7876 \| \| 0.451 \| 33.82 \| 7000 \| 0.4766 \| 0.7830 \| 0.7815 \| \| 0.4483 \| 34.78 \| 7200 \| 0.4779 \| 0.7837 \| 0.7821 \| \| 0.4497 \| 35.75 \| 7400 \| 0.4693 \| 0.7879 \| 0.7867 \| \| 0.4466 \| 36.71 \| 7600 \| 0.4706 \| 0.7886 \| 0.7873 \| \| 0.4458 \| 37.68 \| 7800 \| 0.4705 \| 0.7880 \| 0.7867 \| \| 0.4459 \| 38.65 \| 8000 \| 0.4674 \| 0.7897 \| 0.7888 \| \| 0.4516 \| 39.61 \| 8200 \| 0.4700 \| 0.7889 \| 0.7876 \| \| 0.4434 \| 40.58 \| 8400 \| 0.4737 \| 0.7866 \| 0.7852 \| \| 0.4459 \| 41.55 \| 8600 \| 0.4691 \| 0.7882 \| 0.7870 \| \| 0.4429 \| 42.51 \| 8800 \| 0.4703 \| 0.7895 \| 0.7882 \| \| 0.4472 \| 43.48 \| 9000 \| 0.4776 \| 0.7878 \| 0.7864 \| \| 0.4449 \| 44.44 \| 9200 \| 0.4728 \| 0.7878 \| 0.7864 \| \| 0.4463 \| 45.41 \| 9400 \| 0.4711 \| 0.7874 \| 0.7861 \| \| 0.4401 \| 46.38 \| 9600 \| 0.4737 \| 0.7881 \| 0.7867 \| \| 0.4472 \| 47.34 \| 9800 \| 0.4711 \| 0.7871 \| 0.7858 \| \| 0.445 \| 48.31 \| 10000 \| 0.4707 \| 0.7886 \| 0.7873 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_EMP_H3K14ac-seqsight_4096_512_46M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K14ac-seqsight_4096_512_46M-L1_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_46M", "region:us" ]	null	2024-04-26T20:11:15+00:00	[]	[]	TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us	GUE\_EMP\_H3K14ac-seqsight\_4096\_512\_46M-L1\_f ================================================ This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_46M on the mahdibaghbanzadeh/GUE\_EMP\_H3K14ac dataset. It achieves the following results on the evaluation set: * Loss: 0.4728 * F1 Score: 0.7696 * Accuracy: 0.7691 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]	[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]
null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # GUE_EMP_H3K14ac-seqsight_4096_512_46M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_EMP_H3K14ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K14ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.4760 - F1 Score: 0.7749 - Accuracy: 0.7746 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.5506 \| 0.97 \| 200 \| 0.5042 \| 0.7665 \| 0.7649 \| \| 0.5061 \| 1.93 \| 400 \| 0.4880 \| 0.7721 \| 0.7707 \| \| 0.4929 \| 2.9 \| 600 \| 0.5016 \| 0.7645 \| 0.7631 \| \| 0.4883 \| 3.86 \| 800 \| 0.4736 \| 0.7791 \| 0.7785 \| \| 0.4837 \| 4.83 \| 1000 \| 0.5029 \| 0.7614 \| 0.7604 \| \| 0.4758 \| 5.8 \| 1200 \| 0.4965 \| 0.7623 \| 0.7610 \| \| 0.4725 \| 6.76 \| 1400 \| 0.4706 \| 0.7836 \| 0.7821 \| \| 0.4665 \| 7.73 \| 1600 \| 0.4736 \| 0.7857 \| 0.7843 \| \| 0.4634 \| 8.7 \| 1800 \| 0.4804 \| 0.7809 \| 0.7794 \| \| 0.4562 \| 9.66 \| 2000 \| 0.4784 \| 0.7785 \| 0.7770 \| \| 0.4592 \| 10.63 \| 2200 \| 0.4830 \| 0.7806 \| 0.7791 \| \| 0.4498 \| 11.59 \| 2400 \| 0.4708 \| 0.7844 \| 0.7831 \| \| 0.4515 \| 12.56 \| 2600 \| 0.4800 \| 0.7815 \| 0.7800 \| \| 0.445 \| 13.53 \| 2800 \| 0.4796 \| 0.7728 \| 0.7713 \| \| 0.4446 \| 14.49 \| 3000 \| 0.4770 \| 0.7803 \| 0.7788 \| \| 0.4338 \| 15.46 \| 3200 \| 0.4799 \| 0.7835 \| 0.7825 \| \| 0.4396 \| 16.43 \| 3400 \| 0.4798 \| 0.7797 \| 0.7782 \| \| 0.4335 \| 17.39 \| 3600 \| 0.4743 \| 0.7841 \| 0.7828 \| \| 0.429 \| 18.36 \| 3800 \| 0.4714 \| 0.7858 \| 0.7858 \| \| 0.4269 \| 19.32 \| 4000 \| 0.4705 \| 0.7920 \| 0.7912 \| \| 0.4222 \| 20.29 \| 4200 \| 0.4872 \| 0.7809 \| 0.7800 \| \| 0.426 \| 21.26 \| 4400 \| 0.4792 \| 0.7833 \| 0.7818 \| \| 0.4192 \| 22.22 \| 4600 \| 0.4964 \| 0.7758 \| 0.7743 \| \| 0.418 \| 23.19 \| 4800 \| 0.4780 \| 0.7823 \| 0.7812 \| \| 0.4172 \| 24.15 \| 5000 \| 0.4955 \| 0.7748 \| 0.7734 \| \| 0.4118 \| 25.12 \| 5200 \| 0.5083 \| 0.7752 \| 0.7737 \| \| 0.4093 \| 26.09 \| 5400 \| 0.4897 \| 0.7761 \| 0.7746 \| \| 0.4119 \| 27.05 \| 5600 \| 0.5046 \| 0.7707 \| 0.7691 \| \| 0.4055 \| 28.02 \| 5800 \| 0.4882 \| 0.7847 \| 0.7834 \| \| 0.405 \| 28.99 \| 6000 \| 0.4886 \| 0.7788 \| 0.7773 \| \| 0.4024 \| 29.95 \| 6200 \| 0.4903 \| 0.7714 \| 0.7700 \| \| 0.4001 \| 30.92 \| 6400 \| 0.4825 \| 0.7804 \| 0.7803 \| \| 0.3992 \| 31.88 \| 6600 \| 0.4916 \| 0.7755 \| 0.7746 \| \| 0.3932 \| 32.85 \| 6800 \| 0.5003 \| 0.7751 \| 0.7737 \| \| 0.3965 \| 33.82 \| 7000 \| 0.5031 \| 0.7695 \| 0.7679 \| \| 0.3912 \| 34.78 \| 7200 \| 0.5025 \| 0.7734 \| 0.7719 \| \| 0.3922 \| 35.75 \| 7400 \| 0.4921 \| 0.7713 \| 0.7700 \| \| 0.3893 \| 36.71 \| 7600 \| 0.4995 \| 0.7765 \| 0.7752 \| \| 0.386 \| 37.68 \| 7800 \| 0.5018 \| 0.7730 \| 0.7716 \| \| 0.3874 \| 38.65 \| 8000 \| 0.5012 \| 0.7749 \| 0.7737 \| \| 0.3909 \| 39.61 \| 8200 \| 0.4984 \| 0.7721 \| 0.7710 \| \| 0.382 \| 40.58 \| 8400 \| 0.5084 \| 0.7713 \| 0.7697 \| \| 0.3837 \| 41.55 \| 8600 \| 0.5034 \| 0.7743 \| 0.7731 \| \| 0.3819 \| 42.51 \| 8800 \| 0.5033 \| 0.7757 \| 0.7746 \| \| 0.3829 \| 43.48 \| 9000 \| 0.5079 \| 0.7757 \| 0.7743 \| \| 0.381 \| 44.44 \| 9200 \| 0.5102 \| 0.7727 \| 0.7713 \| \| 0.3843 \| 45.41 \| 9400 \| 0.5049 \| 0.7747 \| 0.7734 \| \| 0.376 \| 46.38 \| 9600 \| 0.5101 \| 0.7730 \| 0.7716 \| \| 0.3797 \| 47.34 \| 9800 \| 0.5075 \| 0.7729 \| 0.7716 \| \| 0.3789 \| 48.31 \| 10000 \| 0.5064 \| 0.7740 \| 0.7728 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_EMP_H3K14ac-seqsight_4096_512_46M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K14ac-seqsight_4096_512_46M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_46M", "region:us" ]	null	2024-04-26T20:13:41+00:00	[]	[]	TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us	GUE\_EMP\_H3K14ac-seqsight\_4096\_512\_46M-L8\_f ================================================ This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_46M on the mahdibaghbanzadeh/GUE\_EMP\_H3K14ac dataset. It achieves the following results on the evaluation set: * Loss: 0.4760 * F1 Score: 0.7749 * Accuracy: 0.7746 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]	[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	AmrMohamed/Meta-Llama-3-8B-grading	null	[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-26T20:14:26+00:00	[ "1910.09700" ]	[]	TAGS #transformers #pytorch #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #pytorch #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # outputs This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "mit", "library_name": "peft", "tags": ["trl", "reward-trainer", "generated_from_trainer"], "base_model": "gpt2", "model-index": [{"name": "outputs", "results": []}]}	AK232003/outputs	null	[ "peft", "safetensors", "trl", "reward-trainer", "generated_from_trainer", "base_model:gpt2", "license:mit", "region:us" ]	null	2024-04-26T20:14:54+00:00	[]	[]	TAGS #peft #safetensors #trl #reward-trainer #generated_from_trainer #base_model-gpt2 #license-mit #region-us	# outputs This model is a fine-tuned version of gpt2 on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	[ "# outputs\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 4\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 10\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- PEFT 0.10.0\n- Transformers 4.40.1\n- Pytorch 2.3.0+cu121\n- Datasets 2.19.0\n- Tokenizers 0.19.1" ]	[ "TAGS\n#peft #safetensors #trl #reward-trainer #generated_from_trainer #base_model-gpt2 #license-mit #region-us \n", "# outputs\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 4\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 10\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- PEFT 0.10.0\n- Transformers 4.40.1\n- Pytorch 2.3.0+cu121\n- Datasets 2.19.0\n- Tokenizers 0.19.1" ]
token-classification	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0623 - Precision: 0.9346 - Recall: 0.9505 - F1: 0.9425 - Accuracy: 0.9864 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0777 \| 1.0 \| 1756 \| 0.0721 \| 0.9129 \| 0.9325 \| 0.9226 \| 0.9814 \| \| 0.036 \| 2.0 \| 3512 \| 0.0604 \| 0.9309 \| 0.9477 \| 0.9392 \| 0.9859 \| \| 0.0186 \| 3.0 \| 5268 \| 0.0623 \| 0.9346 \| 0.9505 \| 0.9425 \| 0.9864 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.1+cpu - Datasets 2.19.0 - Tokenizers 0.15.2	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "bert-base-cased", "model-index": [{"name": "bert-finetuned-ner", "results": []}]}	HFDON/bert-finetuned-ner	null	[ "transformers", "safetensors", "bert", "token-classification", "generated_from_trainer", "base_model:bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-26T20:15:58+00:00	[]	[]	TAGS #transformers #safetensors #bert #token-classification #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	bert-finetuned-ner ================== This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.0623 * Precision: 0.9346 * Recall: 0.9505 * F1: 0.9425 * Accuracy: 0.9864 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.2.1+cpu * Datasets 2.19.0 * Tokenizers 0.15.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.1+cpu\n* Datasets 2.19.0\n* Tokenizers 0.15.2" ]	[ "TAGS\n#transformers #safetensors #bert #token-classification #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.1+cpu\n* Datasets 2.19.0\n* Tokenizers 0.15.2" ]
null	null	<style> .title-container { display: flex; justify-content: center; align-items: center; height: 25vh; /* Adjust this value to position the title vertically / } .title { font-size: 2.5em; text-align: center; color: #333; font-family: "lucida sans unicode", "lucida grande", sans-serif; font-style: italic; font-weight: bold; font-variant: small-caps; letter-spacing: 0.05em; padding: 0.5em 0; background: transparent; } .title span { background: -webkit-linear-gradient(45deg, #99E2FF, #FF5C95); -webkit-background-clip: text; -webkit-text-fill-color: transparent; } .custom-table { table-layout: fixed; width: 100%; border-collapse: collapse; margin-top: 0em; } .custom-table td { width: 50%; vertical-align: top; padding: 5px; box-shadow: 0px 0px 0px 0px rgba(0, 0, 0, 0.15); } .custom-image-container { position: relative; width: 100%; margin-bottom: 0em; overflow: hidden; border-radius: 5px; transition: transform .7s; / Smooth transition for the container / } .custom-image-container:hover { transform: scale(1.05); / Scale the container on hover / } .custom-image { width: 100%; height: auto; object-fit: cover; border-radius: 10px; transition: transform .7s; margin-bottom: 0em; } .custom-button { width: auto; height: 20px; object-fit: cover; border-radius: 5px; transition: transform .7s; margin-bottom: 0em; display: block; margin-left: auto; margin-right: auto; } </style> # Trails/Kiseki series - 192 characters/outfits ## __Models created by user [irrel@Civitai](https://civitai.com/user/irrel).__ ## __Originally published [here](https://civitai.com/models/417827).__ This is a set of 192 characters/outfits from the Trails series for use with Pony Diffusion V6 XL or any of its derivatives. I chose to separate them into individual loras for improved accuracy.\ The .zip file contains: 192 lora files (~20MB each) * A folder with 2 text files containing example prompts for each character * kiseki.txt (characters' physical features and outfit) * kiseki-noclothes.txt (characters' physical features only)\ The models should work with most style loras. They are also flexible enough to support simple custom outfits. Some of the more obscure characters might appear a bit stiff as they had to be trained entirely on 3D renders. <table class="custom-table"> <tr> <td> <div class="custom-image-container"> <img class="custom-image" src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/5e2f634f-f54d-4506-bcdb-046ed72bf9de/original=true/02540-8.jpeg" alt="sample2"> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/4fa84748-8c7f-4ebf-9ce0-47c19724e13c/original=true/02222-1349480391.jpeg" alt="sample1"> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/ebc1f9b4-8baa-493d-98b0-b62a585b47b1/original=true/02381-3917646338.jpeg" alt="sample1"> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/ceb352de-d566-4e9c-b1d4-a9e3630974ca/original=true/01617-206421237.jpeg" alt="sample1"> </div> </td> </table> <table class="custom-table"> <td> <div class="custom-image-container"> <img class="custom-image" src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/d6be2e40-c496-4e33-9fee-c4798da8672c/original=true/01618-1856211698.jpeg" alt="sample1"> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/2ba0670a-4404-48f0-8591-06f0b302c8ae/original=true/01691-382581776.jpeg" alt="sample2"> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/d95bcecf-47db-4779-a072-36ad28f1b9da/original=true/01242-123.jpeg" alt="sample1"> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/53ad44f2-9c4d-406a-a9b6-9ea7014168c2/original=true/02308-3917646265.jpeg" alt="sample1"> </div> </td> </tr> </table> <table class="custom-table"> <tr> <td> <div class="custom-image-container"> <img class="custom-image" src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/d7f62f19-8033-4a44-9160-ecb96fafad0e/original=true/01873-4122203611.jpeg" alt="sample1"> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/1484726c-e3ee-4eda-8f39-dccab9827c01/original=true/01712-3795625343.jpeg" alt="sample1"> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/ca9a0c82-ac35-4999-8c0c-8abc0a5f5453/original=true/02299-3917646256.jpeg" alt="sample1"> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/2a7a51c3-88e6-4a8b-a4c1-a240509acd67/original=true/01904-1427461507.jpeg" alt="sample1"> </div> </td> </table> <table class="custom-table"> <td> <div class="custom-image-container"> <img class="custom-image" src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/1a8684cb-5fe2-4942-8247-1e71e0ac20e2/original=true/00926-3008217640.jpeg" alt="sample2"> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/1c34eff0-30b3-4817-a07f-be1eeabe7ba9/original=true/01530-3892649669.jpeg" alt="sample1"> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/a08256a0-d68b-43b4-a02f-ccae086b591f/original=true/01528-2935231619.jpeg" alt="sample1"> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/20d8c0cc-96cb-4ea7-94c9-a4301d21694d/original=true/01370-3301814111.jpeg" alt="sample1"> </div> </td> </tr> </table> ## Included characters: 1. Ada Grant 2. Aeolia 3. Agnes Claudel (5 outfits) 4. Alfin Reise Arnor 5. Alisa Reinford (3 outfits) 6. Altera 7. Altina Orion (5 outfits) 8. Anelace Elfead 9. Angelica Rogner (2 outfits) 10. Annabelle 11. rianrhod 12. shen Lu 13. urelia Le Guin 14. urier Vander 15. Becky 16. Bennet 17. Beryl 18. Cecile Neues (2 outfits) 19. Celine Millstein 20. Celis Ortesia (2 outfits) 21. Claire Rieveldt (3 outfits) 22. Clarice Seeker 23. Diana 24. Dorothee 25. Duvalie (2 outfits) 26. Edel 27. Elaine Auclair (3 outfits) 28. Elie MacDowell (3 outfits) 29. Elise Schwarzer 30. Emily 31. Emma Millstein (3 outfits) 32. Ennea 33. Erika Russell 34. Esmeray Archette (2 outfits) 35. Estelle Bright (3 outfits) 36. Fatma Worzel 37. Feri Al-Fayed (2 outfits) 38. Ferris Florald 39. Fie Claussell (2 outfits) 40. Fiona Craig 41. Flotte 42. Fran Seekr 43. Grace Lynn 44. Grandmaster 45. Grimcats 46. Hal Coleman 47. Hermes 48. Ilya Platiere (2 outfits) 49. Ines 50. Irina Reinford 51. Jessica Schleiden 52. Jingo 53. Jolda 54. Josette Capua (2 outfits) 55. Judith Ranster (2 outfits) 56. Julia Schwar 57. Juna Crawford (3 outfits) 58. Kaela MacMillan 59. Kate (2 outfits) 60. KeA (2 outfits) 61. Kilika Rouran 62. Kisara (2 outfits) 63. Klaudia Von Auslese (3 outfits) 64. Lapis Rosenberg 65. Laura S Arseid 66. Lavian Winslet 67. Leonora 68. Lila (2 outfits) 69. Lina Crawford 70. Linde 71. Louise (2 outfits) 72. Lucrezia Isselee 73. Lucy Seiland 74. Lynn 75. Mare 76. Mariabell Crois (2 outfits) 77. Marielle Ayme 78. Maya 79. Millium Orion 80. Mint 81. Mirabel Aalton 82. Mireille 83. Misty 84. Musse Egret (3 outfits) 85. Nadia Rayne (2 outfits) 86. Naje Berca 87. Nina Fenly 88. Noel Seeker 89. Odette 90. Olympia 91. Patiry 92. Paulette 93. Priscilla Reise Arnor 94. Rebecca 95. Regina 96. Renne Bright (4 outfits) 97. Ries Argent 98. Risette Twinings (3 outfits) 99. Rixia Mao (3 outfits) 100. Rosalie 101. Roselia Millstein (2 versions) 102. Rosine 103. Saara (2 outfits) 104. Sandy 105. Sara Valestein 106. Scherazard Harvey 107. Shahina (2 outfits) 108. Shanshan 109. Sharon Kreuger (2 outfits) 110. Shino 111. Shirley Orlando (3 outfits) 112. Shizuku MacLaine (2 outfits) 113. Shizuna Rem Misurugi (2 outfits) 114. Sonya Baelz 115. Sophia Hayworth 116. Sully Atraid (2 outfits) 117. Tatiana 118. Theresia Caroline 119. Tio Plato (2 outfits) 120. Tita Russell (2 outfits) 121. Towa Herschel (3 outfits) 122. Valerie (2 outfits) 123. Viola 124. Vita Clotilde (2 outfits) 125. Vivi 126. Wendy 127. Zita Asverl	{"license": "cc-by-nc-4.0"}	hhks/kiseki_chars	null	[ "license:cc-by-nc-4.0", "region:us" ]	null	2024-04-26T20:17:23+00:00	[]	[]	TAGS #license-cc-by-nc-4.0 #region-us	<style> .title-container { display: flex; justify-content: center; align-items: center; height: 25vh; /* Adjust this value to position the title vertically / } .title { font-size: 2.5em; text-align: center; color: #333; font-family: "lucida sans unicode", "lucida grande", sans-serif; font-style: italic; font-weight: bold; font-variant: small-caps; letter-spacing: 0.05em; padding: 0.5em 0; background: transparent; } .title span { background: -webkit-linear-gradient(45deg, #99E2FF, #FF5C95); -webkit-background-clip: text; -webkit-text-fill-color: transparent; } .custom-table { table-layout: fixed; width: 100%; border-collapse: collapse; margin-top: 0em; } .custom-table td { width: 50%; vertical-align: top; padding: 5px; box-shadow: 0px 0px 0px 0px rgba(0, 0, 0, 0.15); } .custom-image-container { position: relative; width: 100%; margin-bottom: 0em; overflow: hidden; border-radius: 5px; transition: transform .7s; / Smooth transition for the container / } .custom-image-container:hover { transform: scale(1.05); / Scale the container on hover / } .custom-image { width: 100%; height: auto; object-fit: cover; border-radius: 10px; transition: transform .7s; margin-bottom: 0em; } .custom-button { width: auto; height: 20px; object-fit: cover; border-radius: 5px; transition: transform .7s; margin-bottom: 0em; display: block; margin-left: auto; margin-right: auto; } </style> # Trails/Kiseki series - 192 characters/outfits ## __Models created by user irrel@Civitai.__ ## __Originally published here.__ This is a set of 192 characters/outfits from the Trails series for use with Pony Diffusion V6 XL or any of its derivatives. I chose to separate them into individual loras for improved accuracy.\ The .zip file contains: 192 lora files (~20MB each) * A folder with 2 text files containing example prompts for each character * URL (characters' physical features and outfit) * URL (characters' physical features only)\ The models should work with most style loras. They are also flexible enough to support simple custom outfits. Some of the more obscure characters might appear a bit stiff as they had to be trained entirely on 3D renders. <table class="custom-table"> <tr> <td> <div class="custom-image-container"> <img class="custom-image" src="URL alt="sample2"> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="URL alt="sample1"> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="URL alt="sample1"> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="URL alt="sample1"> </div> </td> </table> <table class="custom-table"> <td> <div class="custom-image-container"> <img class="custom-image" src="URL alt="sample1"> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="URL alt="sample2"> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="URL alt="sample1"> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="URL alt="sample1"> </div> </td> </tr> </table> <table class="custom-table"> <tr> <td> <div class="custom-image-container"> <img class="custom-image" src="URL alt="sample1"> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="URL alt="sample1"> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="URL alt="sample1"> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="URL alt="sample1"> </div> </td> </table> <table class="custom-table"> <td> <div class="custom-image-container"> <img class="custom-image" src="URL alt="sample2"> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="URL alt="sample1"> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="URL alt="sample1"> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="URL alt="sample1"> </div> </td> </tr> </table> ## Included characters: 1. Ada Grant 2. Aeolia 3. Agnes Claudel (5 outfits) 4. Alfin Reise Arnor 5. Alisa Reinford (3 outfits) 6. Altera 7. Altina Orion (5 outfits) 8. Anelace Elfead 9. Angelica Rogner (2 outfits) 10. Annabelle 11. rianrhod 12. shen Lu 13. urelia Le Guin 14. urier Vander 15. Becky 16. Bennet 17. Beryl 18. Cecile Neues (2 outfits) 19. Celine Millstein 20. Celis Ortesia (2 outfits) 21. Claire Rieveldt (3 outfits) 22. Clarice Seeker 23. Diana 24. Dorothee 25. Duvalie (2 outfits) 26. Edel 27. Elaine Auclair (3 outfits) 28. Elie MacDowell (3 outfits) 29. Elise Schwarzer 30. Emily 31. Emma Millstein (3 outfits) 32. Ennea 33. Erika Russell 34. Esmeray Archette (2 outfits) 35. Estelle Bright (3 outfits) 36. Fatma Worzel 37. Feri Al-Fayed (2 outfits) 38. Ferris Florald 39. Fie Claussell (2 outfits) 40. Fiona Craig 41. Flotte 42. Fran Seekr 43. Grace Lynn 44. Grandmaster 45. Grimcats 46. Hal Coleman 47. Hermes 48. Ilya Platiere (2 outfits) 49. Ines 50. Irina Reinford 51. Jessica Schleiden 52. Jingo 53. Jolda 54. Josette Capua (2 outfits) 55. Judith Ranster (2 outfits) 56. Julia Schwar 57. Juna Crawford (3 outfits) 58. Kaela MacMillan 59. Kate (2 outfits) 60. KeA (2 outfits) 61. Kilika Rouran 62. Kisara (2 outfits) 63. Klaudia Von Auslese (3 outfits) 64. Lapis Rosenberg 65. Laura S Arseid 66. Lavian Winslet 67. Leonora 68. Lila (2 outfits) 69. Lina Crawford 70. Linde 71. Louise (2 outfits) 72. Lucrezia Isselee 73. Lucy Seiland 74. Lynn 75. Mare 76. Mariabell Crois (2 outfits) 77. Marielle Ayme 78. Maya 79. Millium Orion 80. Mint 81. Mirabel Aalton 82. Mireille 83. Misty 84. Musse Egret (3 outfits) 85. Nadia Rayne (2 outfits) 86. Naje Berca 87. Nina Fenly 88. Noel Seeker 89. Odette 90. Olympia 91. Patiry 92. Paulette 93. Priscilla Reise Arnor 94. Rebecca 95. Regina 96. Renne Bright (4 outfits) 97. Ries Argent 98. Risette Twinings (3 outfits) 99. Rixia Mao (3 outfits) 100. Rosalie 101. Roselia Millstein (2 versions) 102. Rosine 103. Saara (2 outfits) 104. Sandy 105. Sara Valestein 106. Scherazard Harvey 107. Shahina (2 outfits) 108. Shanshan 109. Sharon Kreuger (2 outfits) 110. Shino 111. Shirley Orlando (3 outfits) 112. Shizuku MacLaine (2 outfits) 113. Shizuna Rem Misurugi (2 outfits) 114. Sonya Baelz 115. Sophia Hayworth 116. Sully Atraid (2 outfits) 117. Tatiana 118. Theresia Caroline 119. Tio Plato (2 outfits) 120. Tita Russell (2 outfits) 121. Towa Herschel (3 outfits) 122. Valerie (2 outfits) 123. Viola 124. Vita Clotilde (2 outfits) 125. Vivi 126. Wendy 127. Zita Asverl	[ "# Trails/Kiseki series - 192 characters/outfits", "## __Models created by user irrel@Civitai.__", "## __Originally published here.__\n\nThis is a set of 192 characters/outfits from the Trails series for use with Pony Diffusion V6 XL or any of its derivatives. I chose to separate them into individual loras for improved accuracy.\\\nThe .zip file contains:\n* 192 lora files (~20MB each)\n* A folder with 2 text files containing example prompts for each character\n* URL (characters' physical features and outfit)\n* URL (characters' physical features only)\\\nThe models should work with most style loras. They are also flexible enough to support simple custom outfits. Some of the more obscure characters might appear a bit stiff as they had to be trained entirely on 3D renders.\n\n\n\n<table class=\"custom-table\">\n <tr>\n <td>\n <div class=\"custom-image-container\">\n <img class=\"custom-image\" src=\"URL alt=\"sample2\">\n </div>\n </td>\n <td>\n <div class=\"custom-image-container\">\n <img class=\"custom-image\" src=\"URL alt=\"sample1\">\n </div>\n </td>\n <td>\n <div class=\"custom-image-container\">\n <img class=\"custom-image\" src=\"URL alt=\"sample1\">\n </div>\n </td>\n <td>\n <div class=\"custom-image-container\">\n <img class=\"custom-image\" src=\"URL alt=\"sample1\">\n </div>\n </td>\n</table>\n<table class=\"custom-table\">\n <td>\n <div class=\"custom-image-container\">\n <img class=\"custom-image\" src=\"URL alt=\"sample1\">\n </div>\n </td>\n <td>\n <div class=\"custom-image-container\">\n <img class=\"custom-image\" src=\"URL alt=\"sample2\">\n </div>\n </td>\n <td>\n <div class=\"custom-image-container\">\n <img class=\"custom-image\" src=\"URL alt=\"sample1\">\n </div>\n </td>\n <td>\n <div class=\"custom-image-container\">\n <img class=\"custom-image\" src=\"URL alt=\"sample1\">\n </div>\n </td>\n </tr>\n</table>\n<table class=\"custom-table\">\n <tr>\n <td>\n <div class=\"custom-image-container\">\n <img class=\"custom-image\" src=\"URL alt=\"sample1\">\n </div>\n </td>\n <td>\n <div class=\"custom-image-container\">\n <img class=\"custom-image\" src=\"URL alt=\"sample1\">\n </div>\n </td>\n <td>\n <div class=\"custom-image-container\">\n <img class=\"custom-image\" src=\"URL alt=\"sample1\">\n </div>\n </td>\n <td>\n <div class=\"custom-image-container\">\n <img class=\"custom-image\" src=\"URL alt=\"sample1\">\n </div>\n </td>\n</table>\n<table class=\"custom-table\">\n <td>\n <div class=\"custom-image-container\">\n <img class=\"custom-image\" src=\"URL alt=\"sample2\">\n </div>\n </td>\n <td>\n <div class=\"custom-image-container\">\n <img class=\"custom-image\" src=\"URL alt=\"sample1\">\n </div>\n </td>\n <td>\n <div class=\"custom-image-container\">\n <img class=\"custom-image\" src=\"URL alt=\"sample1\">\n </div>\n </td>\n <td>\n <div class=\"custom-image-container\">\n <img class=\"custom-image\" src=\"URL alt=\"sample1\">\n </div>\n </td>\n </tr>\n</table>", "## Included characters:\n\n1. Ada Grant\n2. Aeolia\n3. Agnes Claudel (5 outfits)\n4. Alfin Reise Arnor\n5. Alisa Reinford (3 outfits)\n6. Altera\n7. Altina Orion (5 outfits)\n8. Anelace Elfead\n9. Angelica Rogner (2 outfits)\n10. Annabelle\n11. rianrhod\n12. shen Lu\n13. urelia Le Guin\n14. urier Vander\n15. Becky\n16. Bennet\n17. Beryl\n18. Cecile Neues (2 outfits)\n19. Celine Millstein\n20. Celis Ortesia (2 outfits)\n21. Claire Rieveldt (3 outfits)\n22. Clarice Seeker\n23. Diana\n24. Dorothee\n25. Duvalie (2 outfits)\n26. Edel\n27. Elaine Auclair (3 outfits)\n28. Elie MacDowell (3 outfits)\n29. Elise Schwarzer\n30. Emily\n31. Emma Millstein (3 outfits)\n32. Ennea\n33. Erika Russell\n34. Esmeray Archette (2 outfits)\n35. Estelle Bright (3 outfits)\n36. Fatma Worzel\n37. Feri Al-Fayed (2 outfits)\n38. Ferris Florald\n39. Fie Claussell (2 outfits)\n40. Fiona Craig\n41. Flotte\n42. Fran Seekr\n43. Grace Lynn\n44. Grandmaster\n45. Grimcats\n46. Hal Coleman\n47. Hermes\n48. Ilya Platiere (2 outfits)\n49. Ines\n50. Irina Reinford\n51. Jessica Schleiden\n52. Jingo\n53. Jolda\n54. Josette Capua (2 outfits)\n55. Judith Ranster (2 outfits)\n56. Julia Schwar\n57. Juna Crawford (3 outfits)\n58. Kaela MacMillan\n59. Kate (2 outfits)\n60. KeA (2 outfits)\n61. Kilika Rouran\n62. Kisara (2 outfits)\n63. Klaudia Von Auslese (3 outfits)\n64. Lapis Rosenberg\n65. Laura S Arseid\n66. Lavian Winslet\n67. Leonora\n68. Lila (2 outfits)\n69. Lina Crawford\n70. Linde\n71. Louise (2 outfits)\n72. Lucrezia Isselee\n73. Lucy Seiland\n74. Lynn\n75. Mare\n76. Mariabell Crois (2 outfits)\n77. Marielle Ayme\n78. Maya\n79. Millium Orion\n80. Mint\n81. Mirabel Aalton\n82. Mireille\n83. Misty\n84. Musse Egret (3 outfits)\n85. Nadia Rayne (2 outfits)\n86. Naje Berca\n87. Nina Fenly\n88. Noel Seeker\n89. Odette\n90. Olympia\n91. Patiry\n92. Paulette\n93. Priscilla Reise Arnor\n94. Rebecca\n95. Regina\n96. Renne Bright (4 outfits)\n97. Ries Argent\n98. Risette Twinings (3 outfits)\n99. Rixia Mao (3 outfits)\n100. Rosalie\n101. Roselia Millstein (2 versions)\n102. Rosine\n103. Saara (2 outfits)\n104. Sandy\n105. Sara Valestein\n106. Scherazard Harvey\n107. Shahina (2 outfits)\n108. Shanshan\n109. Sharon Kreuger (2 outfits)\n110. Shino\n111. Shirley Orlando (3 outfits)\n112. Shizuku MacLaine (2 outfits)\n113. Shizuna Rem Misurugi (2 outfits)\n114. Sonya Baelz\n115. Sophia Hayworth\n116. Sully Atraid (2 outfits)\n117. Tatiana\n118. Theresia Caroline\n119. Tio Plato (2 outfits)\n120. Tita Russell (2 outfits)\n121. Towa Herschel (3 outfits)\n122. Valerie (2 outfits)\n123. Viola\n124. Vita Clotilde (2 outfits)\n125. Vivi\n126. Wendy\n127. Zita Asverl" ]	[ "TAGS\n#license-cc-by-nc-4.0 #region-us \n", "# Trails/Kiseki series - 192 characters/outfits", "## __Models created by user irrel@Civitai.__", "## __Originally published here.__\n\nThis is a set of 192 characters/outfits from the Trails series for use with Pony Diffusion V6 XL or any of its derivatives. I chose to separate them into individual loras for improved accuracy.\\\nThe .zip file contains:\n* 192 lora files (~20MB each)\n* A folder with 2 text files containing example prompts for each character\n* URL (characters' physical features and outfit)\n* URL (characters' physical features only)\\\nThe models should work with most style loras. They are also flexible enough to support simple custom outfits. Some of the more obscure characters might appear a bit stiff as they had to be trained entirely on 3D renders.\n\n\n\n<table class=\"custom-table\">\n <tr>\n <td>\n <div class=\"custom-image-container\">\n <img class=\"custom-image\" src=\"URL alt=\"sample2\">\n </div>\n </td>\n <td>\n <div class=\"custom-image-container\">\n <img class=\"custom-image\" src=\"URL alt=\"sample1\">\n </div>\n </td>\n <td>\n <div class=\"custom-image-container\">\n <img class=\"custom-image\" src=\"URL alt=\"sample1\">\n </div>\n </td>\n <td>\n <div class=\"custom-image-container\">\n <img class=\"custom-image\" src=\"URL alt=\"sample1\">\n </div>\n </td>\n</table>\n<table class=\"custom-table\">\n <td>\n <div class=\"custom-image-container\">\n <img class=\"custom-image\" src=\"URL alt=\"sample1\">\n </div>\n </td>\n <td>\n <div class=\"custom-image-container\">\n <img class=\"custom-image\" src=\"URL alt=\"sample2\">\n </div>\n </td>\n <td>\n <div class=\"custom-image-container\">\n <img class=\"custom-image\" src=\"URL alt=\"sample1\">\n </div>\n </td>\n <td>\n <div class=\"custom-image-container\">\n <img class=\"custom-image\" src=\"URL alt=\"sample1\">\n </div>\n </td>\n </tr>\n</table>\n<table class=\"custom-table\">\n <tr>\n <td>\n <div class=\"custom-image-container\">\n <img class=\"custom-image\" src=\"URL alt=\"sample1\">\n </div>\n </td>\n <td>\n <div class=\"custom-image-container\">\n <img class=\"custom-image\" src=\"URL alt=\"sample1\">\n </div>\n </td>\n <td>\n <div class=\"custom-image-container\">\n <img class=\"custom-image\" src=\"URL alt=\"sample1\">\n </div>\n </td>\n <td>\n <div class=\"custom-image-container\">\n <img class=\"custom-image\" src=\"URL alt=\"sample1\">\n </div>\n </td>\n</table>\n<table class=\"custom-table\">\n <td>\n <div class=\"custom-image-container\">\n <img class=\"custom-image\" src=\"URL alt=\"sample2\">\n </div>\n </td>\n <td>\n <div class=\"custom-image-container\">\n <img class=\"custom-image\" src=\"URL alt=\"sample1\">\n </div>\n </td>\n <td>\n <div class=\"custom-image-container\">\n <img class=\"custom-image\" src=\"URL alt=\"sample1\">\n </div>\n </td>\n <td>\n <div class=\"custom-image-container\">\n <img class=\"custom-image\" src=\"URL alt=\"sample1\">\n </div>\n </td>\n </tr>\n</table>", "## Included characters:\n\n1. Ada Grant\n2. Aeolia\n3. Agnes Claudel (5 outfits)\n4. Alfin Reise Arnor\n5. Alisa Reinford (3 outfits)\n6. Altera\n7. Altina Orion (5 outfits)\n8. Anelace Elfead\n9. Angelica Rogner (2 outfits)\n10. Annabelle\n11. rianrhod\n12. shen Lu\n13. urelia Le Guin\n14. urier Vander\n15. Becky\n16. Bennet\n17. Beryl\n18. Cecile Neues (2 outfits)\n19. Celine Millstein\n20. Celis Ortesia (2 outfits)\n21. Claire Rieveldt (3 outfits)\n22. Clarice Seeker\n23. Diana\n24. Dorothee\n25. Duvalie (2 outfits)\n26. Edel\n27. Elaine Auclair (3 outfits)\n28. Elie MacDowell (3 outfits)\n29. Elise Schwarzer\n30. Emily\n31. Emma Millstein (3 outfits)\n32. Ennea\n33. Erika Russell\n34. Esmeray Archette (2 outfits)\n35. Estelle Bright (3 outfits)\n36. Fatma Worzel\n37. Feri Al-Fayed (2 outfits)\n38. Ferris Florald\n39. Fie Claussell (2 outfits)\n40. Fiona Craig\n41. Flotte\n42. Fran Seekr\n43. Grace Lynn\n44. Grandmaster\n45. Grimcats\n46. Hal Coleman\n47. Hermes\n48. Ilya Platiere (2 outfits)\n49. Ines\n50. Irina Reinford\n51. Jessica Schleiden\n52. Jingo\n53. Jolda\n54. Josette Capua (2 outfits)\n55. Judith Ranster (2 outfits)\n56. Julia Schwar\n57. Juna Crawford (3 outfits)\n58. Kaela MacMillan\n59. Kate (2 outfits)\n60. KeA (2 outfits)\n61. Kilika Rouran\n62. Kisara (2 outfits)\n63. Klaudia Von Auslese (3 outfits)\n64. Lapis Rosenberg\n65. Laura S Arseid\n66. Lavian Winslet\n67. Leonora\n68. Lila (2 outfits)\n69. Lina Crawford\n70. Linde\n71. Louise (2 outfits)\n72. Lucrezia Isselee\n73. Lucy Seiland\n74. Lynn\n75. Mare\n76. Mariabell Crois (2 outfits)\n77. Marielle Ayme\n78. Maya\n79. Millium Orion\n80. Mint\n81. Mirabel Aalton\n82. Mireille\n83. Misty\n84. Musse Egret (3 outfits)\n85. Nadia Rayne (2 outfits)\n86. Naje Berca\n87. Nina Fenly\n88. Noel Seeker\n89. Odette\n90. Olympia\n91. Patiry\n92. Paulette\n93. Priscilla Reise Arnor\n94. Rebecca\n95. Regina\n96. Renne Bright (4 outfits)\n97. Ries Argent\n98. Risette Twinings (3 outfits)\n99. Rixia Mao (3 outfits)\n100. Rosalie\n101. Roselia Millstein (2 versions)\n102. Rosine\n103. Saara (2 outfits)\n104. Sandy\n105. Sara Valestein\n106. Scherazard Harvey\n107. Shahina (2 outfits)\n108. Shanshan\n109. Sharon Kreuger (2 outfits)\n110. Shino\n111. Shirley Orlando (3 outfits)\n112. Shizuku MacLaine (2 outfits)\n113. Shizuna Rem Misurugi (2 outfits)\n114. Sonya Baelz\n115. Sophia Hayworth\n116. Sully Atraid (2 outfits)\n117. Tatiana\n118. Theresia Caroline\n119. Tio Plato (2 outfits)\n120. Tita Russell (2 outfits)\n121. Towa Herschel (3 outfits)\n122. Valerie (2 outfits)\n123. Viola\n124. Vita Clotilde (2 outfits)\n125. Vivi\n126. Wendy\n127. Zita Asverl" ]
text-classification	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_model This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2276 - Accuracy: 0.9424 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2073 \| 1.0 \| 1563 \| 0.1896 \| 0.9298 \| \| 0.1448 \| 2.0 \| 3126 \| 0.2276 \| 0.9424 \| ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "albert-base-v2", "model-index": [{"name": "my_awesome_model", "results": []}]}	BohanJiang/my_awesome_model	null	[ "transformers", "tensorboard", "safetensors", "albert", "text-classification", "generated_from_trainer", "base_model:albert-base-v2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-26T20:19:30+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #albert #text-classification #generated_from_trainer #base_model-albert-base-v2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	my\_awesome\_model ================== This model is a fine-tuned version of albert-base-v2 on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.2276 * Accuracy: 0.9424 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 2 ### Training results ### Framework versions * Transformers 4.40.0 * Pytorch 2.2.1+cu121 * Datasets 2.19.0 * Tokenizers 0.19.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #albert #text-classification #generated_from_trainer #base_model-albert-base-v2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]
null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # GUE_EMP_H3K14ac-seqsight_4096_512_46M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_EMP_H3K14ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K14ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.4754 - F1 Score: 0.7754 - Accuracy: 0.7746 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.5415 \| 0.97 \| 200 \| 0.4991 \| 0.7599 \| 0.7582 \| \| 0.4981 \| 1.93 \| 400 \| 0.4800 \| 0.7757 \| 0.7743 \| \| 0.4837 \| 2.9 \| 600 \| 0.4962 \| 0.7656 \| 0.7643 \| \| 0.4772 \| 3.86 \| 800 \| 0.4695 \| 0.7792 \| 0.7779 \| \| 0.471 \| 4.83 \| 1000 \| 0.5198 \| 0.7598 \| 0.7589 \| \| 0.4605 \| 5.8 \| 1200 \| 0.4931 \| 0.7705 \| 0.7691 \| \| 0.4537 \| 6.76 \| 1400 \| 0.4735 \| 0.7818 \| 0.7803 \| \| 0.4446 \| 7.73 \| 1600 \| 0.4716 \| 0.7838 \| 0.7825 \| \| 0.4392 \| 8.7 \| 1800 \| 0.4845 \| 0.7800 \| 0.7785 \| \| 0.4285 \| 9.66 \| 2000 \| 0.4860 \| 0.7704 \| 0.7688 \| \| 0.427 \| 10.63 \| 2200 \| 0.5009 \| 0.7794 \| 0.7779 \| \| 0.4138 \| 11.59 \| 2400 \| 0.4853 \| 0.7758 \| 0.7746 \| \| 0.409 \| 12.56 \| 2600 \| 0.4986 \| 0.7805 \| 0.7794 \| \| 0.3984 \| 13.53 \| 2800 \| 0.5008 \| 0.7647 \| 0.7631 \| \| 0.3934 \| 14.49 \| 3000 \| 0.5097 \| 0.7713 \| 0.7697 \| \| 0.377 \| 15.46 \| 3200 \| 0.5298 \| 0.7762 \| 0.7749 \| \| 0.3789 \| 16.43 \| 3400 \| 0.5258 \| 0.7698 \| 0.7682 \| \| 0.3651 \| 17.39 \| 3600 \| 0.5315 \| 0.7672 \| 0.7658 \| \| 0.356 \| 18.36 \| 3800 \| 0.5486 \| 0.7702 \| 0.7688 \| \| 0.3535 \| 19.32 \| 4000 \| 0.5380 \| 0.7740 \| 0.7728 \| \| 0.3368 \| 20.29 \| 4200 \| 0.5776 \| 0.7764 \| 0.7758 \| \| 0.3397 \| 21.26 \| 4400 \| 0.5543 \| 0.7727 \| 0.7713 \| \| 0.3299 \| 22.22 \| 4600 \| 0.5806 \| 0.7677 \| 0.7661 \| \| 0.3246 \| 23.19 \| 4800 \| 0.5656 \| 0.7772 \| 0.7758 \| \| 0.3155 \| 24.15 \| 5000 \| 0.6116 \| 0.7749 \| 0.7734 \| \| 0.3081 \| 25.12 \| 5200 \| 0.5955 \| 0.7653 \| 0.7637 \| \| 0.3004 \| 26.09 \| 5400 \| 0.5893 \| 0.7790 \| 0.7776 \| \| 0.3003 \| 27.05 \| 5600 \| 0.6006 \| 0.7740 \| 0.7725 \| \| 0.2921 \| 28.02 \| 5800 \| 0.6405 \| 0.7692 \| 0.7676 \| \| 0.2845 \| 28.99 \| 6000 \| 0.6178 \| 0.7682 \| 0.7667 \| \| 0.2802 \| 29.95 \| 6200 \| 0.6065 \| 0.7690 \| 0.7676 \| \| 0.2781 \| 30.92 \| 6400 \| 0.5852 \| 0.7805 \| 0.7797 \| \| 0.2693 \| 31.88 \| 6600 \| 0.6314 \| 0.7724 \| 0.7710 \| \| 0.2647 \| 32.85 \| 6800 \| 0.6444 \| 0.7695 \| 0.7679 \| \| 0.2607 \| 33.82 \| 7000 \| 0.6346 \| 0.7745 \| 0.7731 \| \| 0.2542 \| 34.78 \| 7200 \| 0.6513 \| 0.7682 \| 0.7667 \| \| 0.257 \| 35.75 \| 7400 \| 0.6532 \| 0.7611 \| 0.7595 \| \| 0.2466 \| 36.71 \| 7600 \| 0.6450 \| 0.7733 \| 0.7725 \| \| 0.2456 \| 37.68 \| 7800 \| 0.6273 \| 0.7704 \| 0.7691 \| \| 0.2411 \| 38.65 \| 8000 \| 0.6753 \| 0.7705 \| 0.7691 \| \| 0.2438 \| 39.61 \| 8200 \| 0.6777 \| 0.7700 \| 0.7688 \| \| 0.2326 \| 40.58 \| 8400 \| 0.6991 \| 0.7704 \| 0.7688 \| \| 0.2391 \| 41.55 \| 8600 \| 0.6810 \| 0.7670 \| 0.7655 \| \| 0.2335 \| 42.51 \| 8800 \| 0.6759 \| 0.7719 \| 0.7707 \| \| 0.231 \| 43.48 \| 9000 \| 0.6950 \| 0.7715 \| 0.7700 \| \| 0.2292 \| 44.44 \| 9200 \| 0.6988 \| 0.7682 \| 0.7667 \| \| 0.2291 \| 45.41 \| 9400 \| 0.6996 \| 0.7682 \| 0.7667 \| \| 0.2188 \| 46.38 \| 9600 \| 0.7126 \| 0.7703 \| 0.7688 \| \| 0.2218 \| 47.34 \| 9800 \| 0.7034 \| 0.7696 \| 0.7682 \| \| 0.2218 \| 48.31 \| 10000 \| 0.7038 \| 0.7705 \| 0.7691 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_EMP_H3K14ac-seqsight_4096_512_46M-L32_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K14ac-seqsight_4096_512_46M-L32_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_46M", "region:us" ]	null	2024-04-26T20:20:37+00:00	[]	[]	TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us	GUE\_EMP\_H3K14ac-seqsight\_4096\_512\_46M-L32\_f ================================================= This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_46M on the mahdibaghbanzadeh/GUE\_EMP\_H3K14ac dataset. It achieves the following results on the evaluation set: * Loss: 0.4754 * F1 Score: 0.7754 * Accuracy: 0.7746 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]	[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]
null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # GUE_EMP_H3K4me2-seqsight_4096_512_46M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me2) dataset. It achieves the following results on the evaluation set: - Loss: 0.5833 - F1 Score: 0.6924 - Accuracy: 0.6966 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.6394 \| 1.04 \| 200 \| 0.6318 \| 0.5864 \| 0.6422 \| \| 0.6149 \| 2.08 \| 400 \| 0.6151 \| 0.6606 \| 0.6631 \| \| 0.6069 \| 3.12 \| 600 \| 0.6077 \| 0.6453 \| 0.6693 \| \| 0.6023 \| 4.17 \| 800 \| 0.6002 \| 0.6715 \| 0.6748 \| \| 0.5966 \| 5.21 \| 1000 \| 0.6032 \| 0.6610 \| 0.6725 \| \| 0.5931 \| 6.25 \| 1200 \| 0.5932 \| 0.6712 \| 0.6833 \| \| 0.5879 \| 7.29 \| 1400 \| 0.5943 \| 0.6770 \| 0.6781 \| \| 0.5854 \| 8.33 \| 1600 \| 0.5951 \| 0.6791 \| 0.6813 \| \| 0.5836 \| 9.38 \| 1800 \| 0.5983 \| 0.6824 \| 0.6810 \| \| 0.5814 \| 10.42 \| 2000 \| 0.5874 \| 0.6765 \| 0.6846 \| \| 0.582 \| 11.46 \| 2200 \| 0.5864 \| 0.6747 \| 0.6882 \| \| 0.572 \| 12.5 \| 2400 \| 0.5876 \| 0.6792 \| 0.6862 \| \| 0.5725 \| 13.54 \| 2600 \| 0.5870 \| 0.6763 \| 0.6859 \| \| 0.5749 \| 14.58 \| 2800 \| 0.5877 \| 0.6759 \| 0.6872 \| \| 0.5739 \| 15.62 \| 3000 \| 0.5879 \| 0.6828 \| 0.6878 \| \| 0.5675 \| 16.67 \| 3200 \| 0.5866 \| 0.6881 \| 0.6914 \| \| 0.5688 \| 17.71 \| 3400 \| 0.5848 \| 0.6846 \| 0.6905 \| \| 0.5664 \| 18.75 \| 3600 \| 0.5884 \| 0.6729 \| 0.6852 \| \| 0.5685 \| 19.79 \| 3800 \| 0.5850 \| 0.6848 \| 0.6901 \| \| 0.5622 \| 20.83 \| 4000 \| 0.5844 \| 0.6847 \| 0.6882 \| \| 0.5624 \| 21.88 \| 4200 \| 0.5881 \| 0.6838 \| 0.6849 \| \| 0.5596 \| 22.92 \| 4400 \| 0.5862 \| 0.6853 \| 0.6891 \| \| 0.5617 \| 23.96 \| 4600 \| 0.5843 \| 0.6885 \| 0.6898 \| \| 0.5599 \| 25.0 \| 4800 \| 0.5830 \| 0.6886 \| 0.6940 \| \| 0.5584 \| 26.04 \| 5000 \| 0.5874 \| 0.6839 \| 0.6859 \| \| 0.5565 \| 27.08 \| 5200 \| 0.5853 \| 0.6832 \| 0.6843 \| \| 0.5557 \| 28.12 \| 5400 \| 0.5837 \| 0.6852 \| 0.6898 \| \| 0.554 \| 29.17 \| 5600 \| 0.5870 \| 0.6819 \| 0.6852 \| \| 0.5543 \| 30.21 \| 5800 \| 0.5883 \| 0.6856 \| 0.6885 \| \| 0.5509 \| 31.25 \| 6000 \| 0.5886 \| 0.6846 \| 0.6869 \| \| 0.5536 \| 32.29 \| 6200 \| 0.5829 \| 0.6862 \| 0.6918 \| \| 0.5529 \| 33.33 \| 6400 \| 0.5874 \| 0.6859 \| 0.6882 \| \| 0.5495 \| 34.38 \| 6600 \| 0.5847 \| 0.6912 \| 0.6957 \| \| 0.5501 \| 35.42 \| 6800 \| 0.5840 \| 0.6881 \| 0.6927 \| \| 0.5507 \| 36.46 \| 7000 \| 0.5848 \| 0.6868 \| 0.6924 \| \| 0.5482 \| 37.5 \| 7200 \| 0.5848 \| 0.6889 \| 0.6924 \| \| 0.5465 \| 38.54 \| 7400 \| 0.5842 \| 0.6874 \| 0.6918 \| \| 0.5518 \| 39.58 \| 7600 \| 0.5826 \| 0.6908 \| 0.6960 \| \| 0.5495 \| 40.62 \| 7800 \| 0.5877 \| 0.6859 \| 0.6862 \| \| 0.5457 \| 41.67 \| 8000 \| 0.5833 \| 0.6893 \| 0.6921 \| \| 0.544 \| 42.71 \| 8200 \| 0.5836 \| 0.6927 \| 0.6976 \| \| 0.548 \| 43.75 \| 8400 \| 0.5827 \| 0.6925 \| 0.6960 \| \| 0.5431 \| 44.79 \| 8600 \| 0.5845 \| 0.6912 \| 0.6931 \| \| 0.542 \| 45.83 \| 8800 \| 0.5829 \| 0.6928 \| 0.6979 \| \| 0.5444 \| 46.88 \| 9000 \| 0.5848 \| 0.6899 \| 0.6918 \| \| 0.5439 \| 47.92 \| 9200 \| 0.5845 \| 0.6898 \| 0.6921 \| \| 0.5454 \| 48.96 \| 9400 \| 0.5835 \| 0.6933 \| 0.6970 \| \| 0.5424 \| 50.0 \| 9600 \| 0.5845 \| 0.6906 \| 0.6931 \| \| 0.5423 \| 51.04 \| 9800 \| 0.5845 \| 0.6899 \| 0.6927 \| \| 0.5406 \| 52.08 \| 10000 \| 0.5845 \| 0.6905 \| 0.6934 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_EMP_H3K4me2-seqsight_4096_512_46M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K4me2-seqsight_4096_512_46M-L1_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_46M", "region:us" ]	null	2024-04-26T20:20:38+00:00	[]	[]	TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us	GUE\_EMP\_H3K4me2-seqsight\_4096\_512\_46M-L1\_f ================================================ This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_46M on the mahdibaghbanzadeh/GUE\_EMP\_H3K4me2 dataset. It achieves the following results on the evaluation set: * Loss: 0.5833 * F1 Score: 0.6924 * Accuracy: 0.6966 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]	[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]
text2text-generation	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # amtibot0 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.7046 - Rouge1: 0.3003 - Rouge2: 0.1181 - Rougel: 0.2653 - Rougelsum: 0.2653 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 10 \| 4.1094 \| 0.3037 \| 0.1191 \| 0.2643 \| 0.2644 \| 19.0 \| \| No log \| 2.0 \| 20 \| 3.8791 \| 0.3019 \| 0.1174 \| 0.2645 \| 0.2644 \| 19.0 \| \| No log \| 3.0 \| 30 \| 3.7484 \| 0.3031 \| 0.1202 \| 0.2678 \| 0.268 \| 19.0 \| \| No log \| 4.0 \| 40 \| 3.7046 \| 0.3003 \| 0.1181 \| 0.2653 \| 0.2653 \| 19.0 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "t5-small", "model-index": [{"name": "amtibot0", "results": []}]}	josiahgottfried/amtibot0	null	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:t5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-26T20:23:52+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-t5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	amtibot0 ======== This model is a fine-tuned version of t5-small on the None dataset. It achieves the following results on the evaluation set: * Loss: 3.7046 * Rouge1: 0.3003 * Rouge2: 0.1181 * Rougel: 0.2653 * Rougelsum: 0.2653 * Gen Len: 19.0 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 32 * eval\_batch\_size: 32 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 4 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.39.3 * Pytorch 2.1.2 * Datasets 2.18.0 * Tokenizers 0.15.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.39.3\n* Pytorch 2.1.2\n* Datasets 2.18.0\n* Tokenizers 0.15.2" ]	[ "TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-t5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.39.3\n* Pytorch 2.1.2\n* Datasets 2.18.0\n* Tokenizers 0.15.2" ]
null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	HenryCai1129/adapter-llama-adapterhappy2sad-1k-50-0.006	null	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-04-26T20:27:58+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
text2text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	gubartz/best_p	null	[ "transformers", "safetensors", "longt5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-26T20:29:17+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #longt5 #text2text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #longt5 #text2text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistral-7b-hf-platypus-lamini-vxxiii-chat-real_baselora This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.37.1 - Pytorch 2.2.0+cu121 - Datasets 2.14.6 - Tokenizers 0.15.1	{"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "mistral-7b-hf-platypus-lamini-vxxiii-chat-real_baselora", "results": []}]}	NassimB/mistral-7b-hf-platypus-lamini-vxxiii-chat-real_baselora	null	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-04-26T20:30:04+00:00	[]	[]	TAGS #peft #safetensors #trl #sft #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us	# mistral-7b-hf-platypus-lamini-vxxiii-chat-real_baselora This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.37.1 - Pytorch 2.2.0+cu121 - Datasets 2.14.6 - Tokenizers 0.15.1	[ "# mistral-7b-hf-platypus-lamini-vxxiii-chat-real_baselora\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0003\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 100\n- num_epochs: 1\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.1\n- Pytorch 2.2.0+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.1" ]	[ "TAGS\n#peft #safetensors #trl #sft #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us \n", "# mistral-7b-hf-platypus-lamini-vxxiii-chat-real_baselora\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0003\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 100\n- num_epochs: 1\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.1\n- Pytorch 2.2.0+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.1" ]
text-generation	transformers	# Keiana-L3-Test5.76-8B-13.6 Keiana-L3-Test5.76-8B-13.6 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): # Keep in mind that, this merged model isn't usually tested at the moment, which could benefit in vocabulary error. * [Kaoeiri/Keiana-L3-Test5.2-8B-8](https://huggingface.co/Kaoeiri/Keiana-L3-Test5.2-8B-8) * [Undi95/Llama-3-LewdPlay-8B](https://huggingface.co/Undi95/Llama-3-LewdPlay-8B) * [Kaoeiri/Keiana-L3-Test4.7-8B-3](https://huggingface.co/Kaoeiri/Keiana-L3-Test4.7-8B-3) ## 🧩 Configuration ```yaml merge_method: model_stock dtype: float16 base_model: Kaoeiri/Keiana-L3-Test5.6-8B-12 models: - model: Kaoeiri/Keiana-L3-Test5.2-8B-8 parameters: weight: .4 density: .4 - model: Undi95/Llama-3-LewdPlay-8B parameters: weight: .2 density: .4 - model: Kaoeiri/Keiana-L3-Test4.7-8B-3 parameters: weight: .1 density: .2 parameters: int8_mask: true ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Kaoeiri/Keiana-L3-Test5.76-8B-13.6" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```	{"tags": ["merge", "mergekit", "lazymergekit", "Kaoeiri/Keiana-L3-Test5.2-8B-8", "Undi95/Llama-3-LewdPlay-8B", "Kaoeiri/Keiana-L3-Test4.7-8B-3"], "base_model": ["Kaoeiri/Keiana-L3-Test5.2-8B-8", "Undi95/Llama-3-LewdPlay-8B", "Kaoeiri/Keiana-L3-Test4.7-8B-3"]}	Kaoeiri/Keiana-L3-Test5.76-8B-13.6	null	[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "lazymergekit", "Kaoeiri/Keiana-L3-Test5.2-8B-8", "Undi95/Llama-3-LewdPlay-8B", "Kaoeiri/Keiana-L3-Test4.7-8B-3", "conversational", "base_model:Kaoeiri/Keiana-L3-Test5.2-8B-8", "base_model:Undi95/Llama-3-LewdPlay-8B", "base_model:Kaoeiri/Keiana-L3-Test4.7-8B-3", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-26T20:30:44+00:00	[]	[]	TAGS #transformers #safetensors #llama #text-generation #merge #mergekit #lazymergekit #Kaoeiri/Keiana-L3-Test5.2-8B-8 #Undi95/Llama-3-LewdPlay-8B #Kaoeiri/Keiana-L3-Test4.7-8B-3 #conversational #base_model-Kaoeiri/Keiana-L3-Test5.2-8B-8 #base_model-Undi95/Llama-3-LewdPlay-8B #base_model-Kaoeiri/Keiana-L3-Test4.7-8B-3 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Keiana-L3-Test5.76-8B-13.6 Keiana-L3-Test5.76-8B-13.6 is a merge of the following models using LazyMergekit: # Keep in mind that, this merged model isn't usually tested at the moment, which could benefit in vocabulary error. * Kaoeiri/Keiana-L3-Test5.2-8B-8 * Undi95/Llama-3-LewdPlay-8B * Kaoeiri/Keiana-L3-Test4.7-8B-3 ## Configuration ## Usage	[ "# Keiana-L3-Test5.76-8B-13.6\n\nKeiana-L3-Test5.76-8B-13.6 is a merge of the following models using LazyMergekit:", "# Keep in mind that, this merged model isn't usually tested at the moment, which could benefit in vocabulary error.\n* Kaoeiri/Keiana-L3-Test5.2-8B-8\n* Undi95/Llama-3-LewdPlay-8B\n* Kaoeiri/Keiana-L3-Test4.7-8B-3", "## Configuration", "## Usage" ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #merge #mergekit #lazymergekit #Kaoeiri/Keiana-L3-Test5.2-8B-8 #Undi95/Llama-3-LewdPlay-8B #Kaoeiri/Keiana-L3-Test4.7-8B-3 #conversational #base_model-Kaoeiri/Keiana-L3-Test5.2-8B-8 #base_model-Undi95/Llama-3-LewdPlay-8B #base_model-Kaoeiri/Keiana-L3-Test4.7-8B-3 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Keiana-L3-Test5.76-8B-13.6\n\nKeiana-L3-Test5.76-8B-13.6 is a merge of the following models using LazyMergekit:", "# Keep in mind that, this merged model isn't usually tested at the moment, which could benefit in vocabulary error.\n* Kaoeiri/Keiana-L3-Test5.2-8B-8\n* Undi95/Llama-3-LewdPlay-8B\n* Kaoeiri/Keiana-L3-Test4.7-8B-3", "## Configuration", "## Usage" ]
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	MrezaPRZ/CodeLLama_SFT_GRETEL	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-26T20:32:16+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
text2text-generation	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Terjman-Large This is a translation model from English to Moroccan darija. It is a finetuned version of "Helsinki-NLP/opus-mt-tc-big-en-ar" on the "BounharAbdelaziz/English-to-Moroccan-Darija" dataset. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 96 - eval_batch_size: 96 - seed: 42 - warmup_ratio: 0.03 - gradient_accumulation_steps: 2 - total_train_batch_size: 182 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Framework versions - Transformers 4.39.2 - Pytorch 2.2.2+cpu - Datasets 2.18.0 - Tokenizers 0.15.2	{"language": ["ar", "en"], "tags": ["generated_from_trainer"], "datasets": ["BounharAbdelaziz/English-to-Moroccan-Darija"], "metrics": ["bleu"], "model-index": [{"name": "results", "results": []}]}	atlasia/Terjman-Large	null	[ "transformers", "safetensors", "marian", "text2text-generation", "generated_from_trainer", "ar", "en", "dataset:BounharAbdelaziz/English-to-Moroccan-Darija", "autotrain_compatible", "endpoints_compatible", "region:us", "has_space" ]	null	2024-04-26T20:33:18+00:00	[]	[ "ar", "en" ]	TAGS #transformers #safetensors #marian #text2text-generation #generated_from_trainer #ar #en #dataset-BounharAbdelaziz/English-to-Moroccan-Darija #autotrain_compatible #endpoints_compatible #region-us #has_space	# Terjman-Large This is a translation model from English to Moroccan darija. It is a finetuned version of "Helsinki-NLP/opus-mt-tc-big-en-ar" on the "BounharAbdelaziz/English-to-Moroccan-Darija" dataset. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 96 - eval_batch_size: 96 - seed: 42 - warmup_ratio: 0.03 - gradient_accumulation_steps: 2 - total_train_batch_size: 182 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Framework versions - Transformers 4.39.2 - Pytorch 2.2.2+cpu - Datasets 2.18.0 - Tokenizers 0.15.2	[ "# Terjman-Large\n\nThis is a translation model from English to Moroccan darija. It is a finetuned version of \"Helsinki-NLP/opus-mt-tc-big-en-ar\" on the \"BounharAbdelaziz/English-to-Moroccan-Darija\" dataset.", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 3e-05\n- train_batch_size: 96\n- eval_batch_size: 96\n- seed: 42\n- warmup_ratio: 0.03\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 182\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 30", "### Framework versions\n\n- Transformers 4.39.2\n- Pytorch 2.2.2+cpu\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]	[ "TAGS\n#transformers #safetensors #marian #text2text-generation #generated_from_trainer #ar #en #dataset-BounharAbdelaziz/English-to-Moroccan-Darija #autotrain_compatible #endpoints_compatible #region-us #has_space \n", "# Terjman-Large\n\nThis is a translation model from English to Moroccan darija. It is a finetuned version of \"Helsinki-NLP/opus-mt-tc-big-en-ar\" on the \"BounharAbdelaziz/English-to-Moroccan-Darija\" dataset.", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 3e-05\n- train_batch_size: 96\n- eval_batch_size: 96\n- seed: 42\n- warmup_ratio: 0.03\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 182\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 30", "### Framework versions\n\n- Transformers 4.39.2\n- Pytorch 2.2.2+cpu\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]
text-classification	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # danish-bert-botxo-fb-housing-posts This model is a fine-tuned version of [Maltehb/danish-bert-botxo](https://huggingface.co/Maltehb/danish-bert-botxo) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1659 - Accuracy: 0.9519 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.372 \| 1.0 \| 55 \| 0.2514 \| 0.9251 \| \| 0.171 \| 2.0 \| 110 \| 0.1881 \| 0.9305 \| \| 0.2315 \| 3.0 \| 165 \| 0.1854 \| 0.9465 \| \| 0.1284 \| 4.0 \| 220 \| 0.1745 \| 0.9465 \| \| 0.0353 \| 5.0 \| 275 \| 0.1659 \| 0.9519 \| ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "Maltehb/danish-bert-botxo", "model-index": [{"name": "danish-bert-botxo-fb-housing-posts", "results": []}]}	hoaj/danish-bert-botxo-fb-housing-posts	null	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:Maltehb/danish-bert-botxo", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-26T20:36:20+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-Maltehb/danish-bert-botxo #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us	danish-bert-botxo-fb-housing-posts ================================== This model is a fine-tuned version of Maltehb/danish-bert-botxo on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.1659 * Accuracy: 0.9519 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 5 ### Training results ### Framework versions * Transformers 4.40.0 * Pytorch 2.2.1+cu121 * Datasets 2.19.0 * Tokenizers 0.19.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-Maltehb/danish-bert-botxo #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]
null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # GUE_EMP_H3K4me2-seqsight_4096_512_46M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me2) dataset. It achieves the following results on the evaluation set: - Loss: 0.5959 - F1 Score: 0.6921 - Accuracy: 0.6927 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.6344 \| 1.04 \| 200 \| 0.6225 \| 0.6144 \| 0.6575 \| \| 0.6043 \| 2.08 \| 400 \| 0.6163 \| 0.6645 \| 0.6631 \| \| 0.5927 \| 3.12 \| 600 \| 0.5941 \| 0.6726 \| 0.6839 \| \| 0.5884 \| 4.17 \| 800 \| 0.5944 \| 0.6772 \| 0.6787 \| \| 0.5821 \| 5.21 \| 1000 \| 0.5999 \| 0.6648 \| 0.6771 \| \| 0.5781 \| 6.25 \| 1200 \| 0.5889 \| 0.6766 \| 0.6830 \| \| 0.57 \| 7.29 \| 1400 \| 0.5864 \| 0.6840 \| 0.6852 \| \| 0.5676 \| 8.33 \| 1600 \| 0.5910 \| 0.6843 \| 0.6865 \| \| 0.5642 \| 9.38 \| 1800 \| 0.5859 \| 0.6850 \| 0.6849 \| \| 0.5589 \| 10.42 \| 2000 \| 0.5879 \| 0.6746 \| 0.6856 \| \| 0.5594 \| 11.46 \| 2200 \| 0.5878 \| 0.6760 \| 0.6888 \| \| 0.5474 \| 12.5 \| 2400 \| 0.5921 \| 0.6837 \| 0.6882 \| \| 0.547 \| 13.54 \| 2600 \| 0.5910 \| 0.6845 \| 0.6875 \| \| 0.546 \| 14.58 \| 2800 \| 0.5962 \| 0.6778 \| 0.6888 \| \| 0.5445 \| 15.62 \| 3000 \| 0.5916 \| 0.6875 \| 0.6908 \| \| 0.5372 \| 16.67 \| 3200 \| 0.5967 \| 0.6834 \| 0.6869 \| \| 0.5345 \| 17.71 \| 3400 \| 0.5937 \| 0.6843 \| 0.6869 \| \| 0.5332 \| 18.75 \| 3600 \| 0.5958 \| 0.6911 \| 0.6973 \| \| 0.5322 \| 19.79 \| 3800 \| 0.5955 \| 0.6925 \| 0.6931 \| \| 0.5259 \| 20.83 \| 4000 \| 0.5892 \| 0.6922 \| 0.6973 \| \| 0.5225 \| 21.88 \| 4200 \| 0.6037 \| 0.6956 \| 0.6973 \| \| 0.5192 \| 22.92 \| 4400 \| 0.6015 \| 0.6874 \| 0.6885 \| \| 0.5181 \| 23.96 \| 4600 \| 0.6004 \| 0.6907 \| 0.6905 \| \| 0.5149 \| 25.0 \| 4800 \| 0.6000 \| 0.6964 \| 0.6966 \| \| 0.5119 \| 26.04 \| 5000 \| 0.6099 \| 0.6871 \| 0.6891 \| \| 0.5081 \| 27.08 \| 5200 \| 0.6031 \| 0.6916 \| 0.6924 \| \| 0.5067 \| 28.12 \| 5400 \| 0.6027 \| 0.6900 \| 0.6937 \| \| 0.5007 \| 29.17 \| 5600 \| 0.6006 \| 0.6901 \| 0.6937 \| \| 0.4993 \| 30.21 \| 5800 \| 0.6142 \| 0.6915 \| 0.6934 \| \| 0.4967 \| 31.25 \| 6000 \| 0.6157 \| 0.6846 \| 0.6856 \| \| 0.4958 \| 32.29 \| 6200 \| 0.6117 \| 0.6835 \| 0.6872 \| \| 0.4951 \| 33.33 \| 6400 \| 0.6141 \| 0.6894 \| 0.6891 \| \| 0.489 \| 34.38 \| 6600 \| 0.6234 \| 0.6854 \| 0.6905 \| \| 0.4901 \| 35.42 \| 6800 \| 0.6234 \| 0.6812 \| 0.6833 \| \| 0.4895 \| 36.46 \| 7000 \| 0.6140 \| 0.6878 \| 0.6888 \| \| 0.4838 \| 37.5 \| 7200 \| 0.6209 \| 0.6917 \| 0.6927 \| \| 0.4808 \| 38.54 \| 7400 \| 0.6218 \| 0.6866 \| 0.6895 \| \| 0.4883 \| 39.58 \| 7600 \| 0.6064 \| 0.6914 \| 0.6970 \| \| 0.4815 \| 40.62 \| 7800 \| 0.6235 \| 0.6883 \| 0.6878 \| \| 0.4764 \| 41.67 \| 8000 \| 0.6188 \| 0.6831 \| 0.6849 \| \| 0.473 \| 42.71 \| 8200 \| 0.6329 \| 0.6819 \| 0.6833 \| \| 0.4764 \| 43.75 \| 8400 \| 0.6181 \| 0.6878 \| 0.6901 \| \| 0.473 \| 44.79 \| 8600 \| 0.6212 \| 0.6869 \| 0.6885 \| \| 0.4728 \| 45.83 \| 8800 \| 0.6212 \| 0.6860 \| 0.6882 \| \| 0.4713 \| 46.88 \| 9000 \| 0.6265 \| 0.6852 \| 0.6856 \| \| 0.472 \| 47.92 \| 9200 \| 0.6250 \| 0.6842 \| 0.6852 \| \| 0.4705 \| 48.96 \| 9400 \| 0.6273 \| 0.6846 \| 0.6875 \| \| 0.4695 \| 50.0 \| 9600 \| 0.6263 \| 0.6861 \| 0.6865 \| \| 0.4686 \| 51.04 \| 9800 \| 0.6281 \| 0.6839 \| 0.6849 \| \| 0.4644 \| 52.08 \| 10000 \| 0.6287 \| 0.6842 \| 0.6849 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_EMP_H3K4me2-seqsight_4096_512_46M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K4me2-seqsight_4096_512_46M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_46M", "region:us" ]	null	2024-04-26T20:40:14+00:00	[]	[]	TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us	GUE\_EMP\_H3K4me2-seqsight\_4096\_512\_46M-L8\_f ================================================ This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_46M on the mahdibaghbanzadeh/GUE\_EMP\_H3K4me2 dataset. It achieves the following results on the evaluation set: * Loss: 0.5959 * F1 Score: 0.6921 * Accuracy: 0.6927 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]	[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]
text-generation	transformers	# GPT-2 Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in [this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) and first released at [this page](https://openai.com/blog/better-language-models/). Disclaimer: The team releasing GPT-2 also wrote a [model card](https://github.com/openai/gpt-2/blob/master/model_card.md) for their model. Content from this model card has been written by the Hugging Face team to complete the information they provided and give specific examples of bias. ## Model description GPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was trained to guess the next word in sentences. More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence, shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a prompt. This is the smallest version of GPT-2, with 124M parameters. Related Models: [GPT-Large](https://huggingface.co/gpt2-large), [GPT-Medium](https://huggingface.co/gpt2-medium) and [GPT-XL](https://huggingface.co/gpt2-xl) ## Intended uses & limitations You can use the raw model for text generation or fine-tune it to a downstream task. See the [model hub](https://huggingface.co/models?filter=gpt2) to look for fine-tuned versions on a task that interests you. ### How to use You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility: ```python >>> from transformers import pipeline, set_seed >>> generator = pipeline('text-generation', model='gpt2') >>> set_seed(42) >>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5) [{'generated_text': "Hello, I'm a language model, a language for thinking, a language for expressing thoughts."}, {'generated_text': "Hello, I'm a language model, a compiler, a compiler library, I just want to know how I build this kind of stuff. I don"}, {'generated_text': "Hello, I'm a language model, and also have more than a few of your own, but I understand that they're going to need some help"}, {'generated_text': "Hello, I'm a language model, a system model. I want to know my language so that it might be more interesting, more user-friendly"}, {'generated_text': 'Hello, I\'m a language model, not a language model"\n\nThe concept of "no-tricks" comes in handy later with new'}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import GPT2Tokenizer, GPT2Model tokenizer = GPT2Tokenizer.from_pretrained('gpt2') model = GPT2Model.from_pretrained('gpt2') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import GPT2Tokenizer, TFGPT2Model tokenizer = GPT2Tokenizer.from_pretrained('gpt2') model = TFGPT2Model.from_pretrained('gpt2') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ### Limitations and bias The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their [model card](https://github.com/openai/gpt-2/blob/master/model_card.md#out-of-scope-use-cases): > Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases > that require the generated text to be true. > > Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do > not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a > study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race, > and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar > levels of caution around use cases that are sensitive to biases around human attributes. Here's an example of how the model can have biased predictions: ```python >>> from transformers import pipeline, set_seed >>> generator = pipeline('text-generation', model='gpt2') >>> set_seed(42) >>> generator("The White man worked as a", max_length=10, num_return_sequences=5) [{'generated_text': 'The White man worked as a mannequin for'}, {'generated_text': 'The White man worked as a maniser of the'}, {'generated_text': 'The White man worked as a bus conductor by day'}, {'generated_text': 'The White man worked as a plumber at the'}, {'generated_text': 'The White man worked as a journalist. He had'}] >>> set_seed(42) >>> generator("The Black man worked as a", max_length=10, num_return_sequences=5) [{'generated_text': 'The Black man worked as a man at a restaurant'}, {'generated_text': 'The Black man worked as a car salesman in a'}, {'generated_text': 'The Black man worked as a police sergeant at the'}, {'generated_text': 'The Black man worked as a man-eating monster'}, {'generated_text': 'The Black man worked as a slave, and was'}] ``` This bias will also affect all fine-tuned versions of this model. ## Training data The OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web pages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from this dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights 40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText [here](https://github.com/openai/gpt-2/blob/master/domains.txt). ## Training procedure ### Preprocessing The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a vocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens. The larger model was trained on 256 cloud TPU v3 cores. The training duration was not disclosed, nor were the exact details of training. ## Evaluation results The model achieves the following results without any fine-tuning (zero-shot): \| Dataset \| LAMBADA \| LAMBADA \| CBT-CN \| CBT-NE \| WikiText2 \| PTB \| enwiki8 \| text8 \| WikiText103 \| 1BW \| \|:--------:\|:-------:\|:-------:\|:------:\|:------:\|:---------:\|:------:\|:-------:\|:------:\|:-----------:\|:-----:\| \| (metric) \| (PPL) \| (ACC) \| (ACC) \| (ACC) \| (PPL) \| (PPL) \| (BPB) \| (BPC) \| (PPL) \| (PPL) \| \| \| 35.13 \| 45.99 \| 87.65 \| 83.4 \| 29.41 \| 65.85 \| 1.16 \| 1,17 \| 37.50 \| 75.20 \| ### BibTeX entry and citation info ```bibtex @article{radford2019language, title={Language Models are Unsupervised Multitask Learners}, author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya}, year={2019} } ``` <a href="https://huggingface.co/exbert/?model=gpt2"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>	{"language": "en", "license": "mit", "tags": ["exbert"]}	ChatTechAI/Babbage-V1	null	[ "transformers", "pytorch", "tf", "jax", "tflite", "rust", "onnx", "safetensors", "gpt2", "text-generation", "exbert", "en", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-26T20:40:45+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #tf #jax #tflite #rust #onnx #safetensors #gpt2 #text-generation #exbert #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	GPT-2 ===== Test the whole generation capabilities here: URL Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in this paper and first released at this page. Disclaimer: The team releasing GPT-2 also wrote a model card for their model. Content from this model card has been written by the Hugging Face team to complete the information they provided and give specific examples of bias. Model description ----------------- GPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was trained to guess the next word in sentences. More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence, shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the predictions for the token 'i' only uses the inputs from '1' to 'i' but not the future tokens. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a prompt. This is the smallest version of GPT-2, with 124M parameters. Related Models: GPT-Large, GPT-Medium and GPT-XL Intended uses & limitations --------------------------- You can use the raw model for text generation or fine-tune it to a downstream task. See the model hub to look for fine-tuned versions on a task that interests you. ### How to use You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility: Here is how to use this model to get the features of a given text in PyTorch: and in TensorFlow: ### Limitations and bias The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their model card: > > Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases > that require the generated text to be true. > > > Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do > not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a > study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race, > and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar > levels of caution around use cases that are sensitive to biases around human attributes. > > > Here's an example of how the model can have biased predictions: This bias will also affect all fine-tuned versions of this model. Training data ------------- The OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web pages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from this dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights 40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText here. Training procedure ------------------ ### Preprocessing The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a vocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens. The larger model was trained on 256 cloud TPU v3 cores. The training duration was not disclosed, nor were the exact details of training. Evaluation results ------------------ The model achieves the following results without any fine-tuning (zero-shot): ### BibTeX entry and citation info <a href="URL <img width="300px" src="URL	[ "### How to use\n\n\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we\nset a seed for reproducibility:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:", "### Limitations and bias\n\n\nThe training data used for this model has not been released as a dataset one can browse. We know it contains a lot of\nunfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their\nmodel card:\n\n\n\n> \n> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases\n> that require the generated text to be true.\n> \n> \n> Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do\n> not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a\n> study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race,\n> and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar\n> levels of caution around use cases that are sensitive to biases around human attributes.\n> \n> \n> \n\n\nHere's an example of how the model can have biased predictions:\n\n\nThis bias will also affect all fine-tuned versions of this model.\n\n\nTraining data\n-------------\n\n\nThe OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web\npages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from\nthis dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights\n40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText\nhere.\n\n\nTraining procedure\n------------------", "### Preprocessing\n\n\nThe texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a\nvocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens.\n\n\nThe larger model was trained on 256 cloud TPU v3 cores. The training duration was not disclosed, nor were the exact\ndetails of training.\n\n\nEvaluation results\n------------------\n\n\nThe model achieves the following results without any fine-tuning (zero-shot):", "### BibTeX entry and citation info\n\n\n<a href=\"URL\n<img width=\"300px\" src=\"URL" ]	[ "TAGS\n#transformers #pytorch #tf #jax #tflite #rust #onnx #safetensors #gpt2 #text-generation #exbert #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### How to use\n\n\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we\nset a seed for reproducibility:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:", "### Limitations and bias\n\n\nThe training data used for this model has not been released as a dataset one can browse. We know it contains a lot of\nunfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their\nmodel card:\n\n\n\n> \n> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases\n> that require the generated text to be true.\n> \n> \n> Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do\n> not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a\n> study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race,\n> and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar\n> levels of caution around use cases that are sensitive to biases around human attributes.\n> \n> \n> \n\n\nHere's an example of how the model can have biased predictions:\n\n\nThis bias will also affect all fine-tuned versions of this model.\n\n\nTraining data\n-------------\n\n\nThe OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web\npages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from\nthis dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights\n40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText\nhere.\n\n\nTraining procedure\n------------------", "### Preprocessing\n\n\nThe texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a\nvocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens.\n\n\nThe larger model was trained on 256 cloud TPU v3 cores. The training duration was not disclosed, nor were the exact\ndetails of training.\n\n\nEvaluation results\n------------------\n\n\nThe model achieves the following results without any fine-tuning (zero-shot):", "### BibTeX entry and citation info\n\n\n<a href=\"URL\n<img width=\"300px\" src=\"URL" ]
null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # output This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 120 ### Training results ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"tags": ["generated_from_trainer"], "model-index": [{"name": "output", "results": []}]}	soft-boy/decision_transformer	null	[ "transformers", "tensorboard", "safetensors", "decision_transformer", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2024-04-26T20:41:20+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #decision_transformer #generated_from_trainer #endpoints_compatible #region-us	# output This model is a fine-tuned version of [](URL on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 120 ### Training results ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	[ "# output\n\nThis model is a fine-tuned version of [](URL on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 64\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 120", "### Training results", "### Framework versions\n\n- Transformers 4.40.0\n- Pytorch 2.2.1+cu121\n- Datasets 2.19.0\n- Tokenizers 0.19.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #decision_transformer #generated_from_trainer #endpoints_compatible #region-us \n", "# output\n\nThis model is a fine-tuned version of [](URL on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 64\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 120", "### Training results", "### Framework versions\n\n- Transformers 4.40.0\n- Pytorch 2.2.1+cu121\n- Datasets 2.19.0\n- Tokenizers 0.19.1" ]
null	transformers	## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> weighted/imatrix quants of https://huggingface.co/maywell/miqu-evil-dpo <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/miqu-evil-dpo-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/miqu-evil-dpo-i1-GGUF/resolve/main/miqu-evil-dpo.i1-IQ1_S.gguf) \| i1-IQ1_S \| 14.6 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/miqu-evil-dpo-i1-GGUF/resolve/main/miqu-evil-dpo.i1-IQ1_M.gguf) \| i1-IQ1_M \| 16.0 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/miqu-evil-dpo-i1-GGUF/resolve/main/miqu-evil-dpo.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 18.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/miqu-evil-dpo-i1-GGUF/resolve/main/miqu-evil-dpo.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 20.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/miqu-evil-dpo-i1-GGUF/resolve/main/miqu-evil-dpo.i1-IQ2_S.gguf) \| i1-IQ2_S \| 21.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/miqu-evil-dpo-i1-GGUF/resolve/main/miqu-evil-dpo.i1-IQ2_M.gguf) \| i1-IQ2_M \| 23.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/miqu-evil-dpo-i1-GGUF/resolve/main/miqu-evil-dpo.i1-Q2_K.gguf) \| i1-Q2_K \| 25.6 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/miqu-evil-dpo-i1-GGUF/resolve/main/miqu-evil-dpo.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 26.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/miqu-evil-dpo-i1-GGUF/resolve/main/miqu-evil-dpo.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 28.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/miqu-evil-dpo-i1-GGUF/resolve/main/miqu-evil-dpo.i1-IQ3_S.gguf) \| i1-IQ3_S \| 30.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/miqu-evil-dpo-i1-GGUF/resolve/main/miqu-evil-dpo.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 30.0 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/miqu-evil-dpo-i1-GGUF/resolve/main/miqu-evil-dpo.i1-IQ3_M.gguf) \| i1-IQ3_M \| 31.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/miqu-evil-dpo-i1-GGUF/resolve/main/miqu-evil-dpo.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 33.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/miqu-evil-dpo-i1-GGUF/resolve/main/miqu-evil-dpo.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 36.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/miqu-evil-dpo-i1-GGUF/resolve/main/miqu-evil-dpo.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 36.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/miqu-evil-dpo-i1-GGUF/resolve/main/miqu-evil-dpo.i1-Q4_0.gguf) \| i1-Q4_0 \| 39.1 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/miqu-evil-dpo-i1-GGUF/resolve/main/miqu-evil-dpo.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 39.3 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/miqu-evil-dpo-i1-GGUF/resolve/main/miqu-evil-dpo.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 41.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/miqu-evil-dpo-i1-GGUF/resolve/main/miqu-evil-dpo.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 47.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/miqu-evil-dpo-i1-GGUF/resolve/main/miqu-evil-dpo.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 48.9 \| \| \| [PART 1](https://huggingface.co/mradermacher/miqu-evil-dpo-i1-GGUF/resolve/main/miqu-evil-dpo.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/miqu-evil-dpo-i1-GGUF/resolve/main/miqu-evil-dpo.i1-Q6_K.gguf.part2of2) \| i1-Q6_K \| 56.7 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->	{"language": ["en"], "license": "other", "library_name": "transformers", "tags": ["not-for-all-audiences"], "base_model": "maywell/miqu-evil-dpo", "license_link": "LICENSE", "license_name": "miqu-license", "quantized_by": "mradermacher"}	mradermacher/miqu-evil-dpo-i1-GGUF	null	[ "transformers", "gguf", "not-for-all-audiences", "en", "base_model:maywell/miqu-evil-dpo", "license:other", "endpoints_compatible", "region:us" ]	null	2024-04-26T20:42:21+00:00	[]	[ "en" ]	TAGS #transformers #gguf #not-for-all-audiences #en #base_model-maywell/miqu-evil-dpo #license-other #endpoints_compatible #region-us	About ----- weighted/imatrix quants of URL static quants are available at URL Usage ----- If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. Provided Quants --------------- (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): !URL And here are Artefact2's thoughts on the matter: URL FAQ / Model Request ------------------- See URL for some answers to questions you might have and/or if you want some other model quantized. Thanks ------ I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.	[]	[ "TAGS\n#transformers #gguf #not-for-all-audiences #en #base_model-maywell/miqu-evil-dpo #license-other #endpoints_compatible #region-us \n" ]
image-classification	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Boya1_RMSProp_1-e5_10Epoch_swin-large-patch4-window7-224_fold2 This model is a fine-tuned version of [microsoft/swin-large-patch4-window7-224](https://huggingface.co/microsoft/swin-large-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.1478 - Accuracy: 0.6735 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.2127 \| 1.0 \| 923 \| 1.1541 \| 0.6049 \| \| 0.9204 \| 2.0 \| 1846 \| 1.0192 \| 0.6551 \| \| 0.7902 \| 3.0 \| 2769 \| 0.9565 \| 0.6649 \| \| 0.7847 \| 4.0 \| 3692 \| 0.9757 \| 0.6743 \| \| 0.6816 \| 5.0 \| 4615 \| 0.9792 \| 0.6692 \| \| 0.4595 \| 6.0 \| 5538 \| 1.0390 \| 0.6678 \| \| 0.425 \| 7.0 \| 6461 \| 1.0878 \| 0.6678 \| \| 0.4186 \| 8.0 \| 7384 \| 1.1180 \| 0.6727 \| \| 0.3048 \| 9.0 \| 8307 \| 1.1393 \| 0.6708 \| \| 0.2135 \| 10.0 \| 9230 \| 1.1478 \| 0.6735 \| ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0 - Datasets 2.14.6 - Tokenizers 0.14.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/swin-large-patch4-window7-224", "model-index": [{"name": "Boya1_RMSProp_1-e5_10Epoch_swin-large-patch4-window7-224_fold2", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.6735135135135135, "name": "Accuracy"}]}]}]}	onizukal/Boya1_RMSProp_1-e5_10Epoch_swin-large-patch4-window7-224_fold2	null	[ "transformers", "safetensors", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/swin-large-patch4-window7-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-26T20:42:35+00:00	[]	[]	TAGS #transformers #safetensors #swin #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/swin-large-patch4-window7-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us	Boya1\_RMSProp\_1-e5\_10Epoch\_swin-large-patch4-window7-224\_fold2 =================================================================== This model is a fine-tuned version of microsoft/swin-large-patch4-window7-224 on the imagefolder dataset. It achieves the following results on the evaluation set: * Loss: 1.1478 * Accuracy: 0.6735 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 1e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_ratio: 0.1 * num\_epochs: 10 ### Training results ### Framework versions * Transformers 4.35.0 * Pytorch 2.1.0 * Datasets 2.14.6 * Tokenizers 0.14.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]	[ "TAGS\n#transformers #safetensors #swin #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/swin-large-patch4-window7-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]
text-generation	transformers	# merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [aaditya/OpenBioLLM-Llama3-8B](https://huggingface.co/aaditya/OpenBioLLM-Llama3-8B) * [mattshumer/Llama-3-8B-16K](https://huggingface.co/mattshumer/Llama-3-8B-16K) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: aaditya/OpenBioLLM-Llama3-8B layer_range: [0, 32] - model: mattshumer/Llama-3-8B-16K layer_range: [0, 32] merge_method: slerp base_model: aaditya/OpenBioLLM-Llama3-8B parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```	{"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["aaditya/OpenBioLLM-Llama3-8B", "mattshumer/Llama-3-8B-16K"]}	o2satz/L3_med16	null	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "base_model:aaditya/OpenBioLLM-Llama3-8B", "base_model:mattshumer/Llama-3-8B-16K", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-26T20:43:19+00:00	[]	[]	TAGS #transformers #safetensors #llama #text-generation #mergekit #merge #base_model-aaditya/OpenBioLLM-Llama3-8B #base_model-mattshumer/Llama-3-8B-16K #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# merge This is a merge of pre-trained language models created using mergekit. ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * aaditya/OpenBioLLM-Llama3-8B * mattshumer/Llama-3-8B-16K ### Configuration The following YAML configuration was used to produce this model:	[ "# merge\n\nThis is a merge of pre-trained language models created using mergekit.", "## Merge Details", "### Merge Method\n\nThis model was merged using the SLERP merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* aaditya/OpenBioLLM-Llama3-8B\n* mattshumer/Llama-3-8B-16K", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #base_model-aaditya/OpenBioLLM-Llama3-8B #base_model-mattshumer/Llama-3-8B-16K #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# merge\n\nThis is a merge of pre-trained language models created using mergekit.", "## Merge Details", "### Merge Method\n\nThis model was merged using the SLERP merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* aaditya/OpenBioLLM-Llama3-8B\n* mattshumer/Llama-3-8B-16K", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	woransa/OrpoLlama-3-8B	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-26T20:43:52+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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(2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # GUE_EMP_H3K4me2-seqsight_4096_512_46M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me2) dataset. It achieves the following results on the evaluation set: - Loss: 0.5838 - F1 Score: 0.6957 - Accuracy: 0.6970 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.6296 \| 1.04 \| 200 \| 0.6099 \| 0.6444 \| 0.6719 \| \| 0.5961 \| 2.08 \| 400 \| 0.6079 \| 0.6722 \| 0.6712 \| \| 0.5838 \| 3.12 \| 600 \| 0.5955 \| 0.6792 \| 0.6852 \| \| 0.5789 \| 4.17 \| 800 \| 0.5935 \| 0.6831 \| 0.6849 \| \| 0.5677 \| 5.21 \| 1000 \| 0.5989 \| 0.6704 \| 0.6794 \| \| 0.5599 \| 6.25 \| 1200 \| 0.5914 \| 0.6868 \| 0.6885 \| \| 0.5498 \| 7.29 \| 1400 \| 0.5906 \| 0.6822 \| 0.6852 \| \| 0.5397 \| 8.33 \| 1600 \| 0.5999 \| 0.6849 \| 0.6895 \| \| 0.5339 \| 9.38 \| 1800 \| 0.6028 \| 0.6835 \| 0.6826 \| \| 0.5233 \| 10.42 \| 2000 \| 0.6158 \| 0.6733 \| 0.6839 \| \| 0.5159 \| 11.46 \| 2200 \| 0.6140 \| 0.6795 \| 0.6888 \| \| 0.4976 \| 12.5 \| 2400 \| 0.6165 \| 0.6813 \| 0.6856 \| \| 0.4901 \| 13.54 \| 2600 \| 0.6297 \| 0.6852 \| 0.6869 \| \| 0.483 \| 14.58 \| 2800 \| 0.6441 \| 0.6727 \| 0.6794 \| \| 0.474 \| 15.62 \| 3000 \| 0.6274 \| 0.6781 \| 0.6823 \| \| 0.4618 \| 16.67 \| 3200 \| 0.6527 \| 0.6793 \| 0.6807 \| \| 0.4507 \| 17.71 \| 3400 \| 0.6513 \| 0.6815 \| 0.6826 \| \| 0.4411 \| 18.75 \| 3600 \| 0.6755 \| 0.6822 \| 0.6849 \| \| 0.4325 \| 19.79 \| 3800 \| 0.6762 \| 0.6808 \| 0.6817 \| \| 0.4178 \| 20.83 \| 4000 \| 0.6663 \| 0.6830 \| 0.6885 \| \| 0.4111 \| 21.88 \| 4200 \| 0.7240 \| 0.6796 \| 0.6810 \| \| 0.3942 \| 22.92 \| 4400 \| 0.6946 \| 0.6762 \| 0.6768 \| \| 0.3911 \| 23.96 \| 4600 \| 0.7232 \| 0.6850 \| 0.6839 \| \| 0.3827 \| 25.0 \| 4800 \| 0.7319 \| 0.6730 \| 0.6722 \| \| 0.3718 \| 26.04 \| 5000 \| 0.7550 \| 0.6841 \| 0.6869 \| \| 0.3595 \| 27.08 \| 5200 \| 0.7298 \| 0.6854 \| 0.6846 \| \| 0.3549 \| 28.12 \| 5400 \| 0.7805 \| 0.6755 \| 0.6787 \| \| 0.3477 \| 29.17 \| 5600 \| 0.7857 \| 0.6724 \| 0.6761 \| \| 0.3431 \| 30.21 \| 5800 \| 0.7814 \| 0.6853 \| 0.6862 \| \| 0.3351 \| 31.25 \| 6000 \| 0.8192 \| 0.6722 \| 0.6751 \| \| 0.3288 \| 32.29 \| 6200 \| 0.8572 \| 0.6694 \| 0.6729 \| \| 0.3269 \| 33.33 \| 6400 \| 0.8028 \| 0.6777 \| 0.6768 \| \| 0.3173 \| 34.38 \| 6600 \| 0.8300 \| 0.6711 \| 0.6732 \| \| 0.3119 \| 35.42 \| 6800 \| 0.8257 \| 0.6722 \| 0.6732 \| \| 0.3029 \| 36.46 \| 7000 \| 0.8348 \| 0.6721 \| 0.6725 \| \| 0.3015 \| 37.5 \| 7200 \| 0.8823 \| 0.6728 \| 0.6738 \| \| 0.2983 \| 38.54 \| 7400 \| 0.8730 \| 0.6689 \| 0.6716 \| \| 0.2983 \| 39.58 \| 7600 \| 0.8422 \| 0.6660 \| 0.6706 \| \| 0.2871 \| 40.62 \| 7800 \| 0.8960 \| 0.6723 \| 0.6732 \| \| 0.2812 \| 41.67 \| 8000 \| 0.9115 \| 0.6643 \| 0.6641 \| \| 0.2769 \| 42.71 \| 8200 \| 0.9336 \| 0.6722 \| 0.6745 \| \| 0.2743 \| 43.75 \| 8400 \| 0.9085 \| 0.6730 \| 0.6738 \| \| 0.2707 \| 44.79 \| 8600 \| 0.9092 \| 0.6707 \| 0.6716 \| \| 0.2732 \| 45.83 \| 8800 \| 0.9188 \| 0.6715 \| 0.6735 \| \| 0.2715 \| 46.88 \| 9000 \| 0.9204 \| 0.6747 \| 0.6755 \| \| 0.2623 \| 47.92 \| 9200 \| 0.9272 \| 0.6707 \| 0.6712 \| \| 0.2654 \| 48.96 \| 9400 \| 0.9270 \| 0.6700 \| 0.6716 \| \| 0.2598 \| 50.0 \| 9600 \| 0.9354 \| 0.6737 \| 0.6738 \| \| 0.2611 \| 51.04 \| 9800 \| 0.9368 \| 0.6737 \| 0.6742 \| \| 0.2565 \| 52.08 \| 10000 \| 0.9348 \| 0.6711 \| 0.6716 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_EMP_H3K4me2-seqsight_4096_512_46M-L32_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K4me2-seqsight_4096_512_46M-L32_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_46M", "region:us" ]	null	2024-04-26T20:44:24+00:00	[]	[]	TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us	GUE\_EMP\_H3K4me2-seqsight\_4096\_512\_46M-L32\_f ================================================= This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_46M on the mahdibaghbanzadeh/GUE\_EMP\_H3K4me2 dataset. It achieves the following results on the evaluation set: * Loss: 0.5838 * F1 Score: 0.6957 * Accuracy: 0.6970 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]	[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]
null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # GUE_EMP_H3K9ac-seqsight_4096_512_46M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_EMP_H3K9ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K9ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.4618 - F1 Score: 0.8001 - Accuracy: 0.7996 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.5729 \| 1.15 \| 200 \| 0.5451 \| 0.7280 \| 0.7290 \| \| 0.5306 \| 2.3 \| 400 \| 0.5608 \| 0.7185 \| 0.7200 \| \| 0.512 \| 3.45 \| 600 \| 0.5266 \| 0.7362 \| 0.7359 \| \| 0.5029 \| 4.6 \| 800 \| 0.5154 \| 0.7436 \| 0.7431 \| \| 0.4963 \| 5.75 \| 1000 \| 0.5082 \| 0.7440 \| 0.7445 \| \| 0.4907 \| 6.9 \| 1200 \| 0.5116 \| 0.7515 \| 0.7510 \| \| 0.4837 \| 8.05 \| 1400 \| 0.5103 \| 0.7524 \| 0.7521 \| \| 0.48 \| 9.2 \| 1600 \| 0.5221 \| 0.7459 \| 0.7463 \| \| 0.4729 \| 10.34 \| 1800 \| 0.5101 \| 0.7541 \| 0.7539 \| \| 0.4742 \| 11.49 \| 2000 \| 0.5007 \| 0.7596 \| 0.7596 \| \| 0.4669 \| 12.64 \| 2200 \| 0.5137 \| 0.7549 \| 0.7546 \| \| 0.4675 \| 13.79 \| 2400 \| 0.4950 \| 0.7656 \| 0.7654 \| \| 0.4648 \| 14.94 \| 2600 \| 0.4951 \| 0.7651 \| 0.7647 \| \| 0.4611 \| 16.09 \| 2800 \| 0.5000 \| 0.7629 \| 0.7625 \| \| 0.4573 \| 17.24 \| 3000 \| 0.5075 \| 0.7616 \| 0.7611 \| \| 0.4572 \| 18.39 \| 3200 \| 0.5053 \| 0.7625 \| 0.7621 \| \| 0.4581 \| 19.54 \| 3400 \| 0.4920 \| 0.7652 \| 0.7647 \| \| 0.4508 \| 20.69 \| 3600 \| 0.4946 \| 0.7632 \| 0.7632 \| \| 0.4475 \| 21.84 \| 3800 \| 0.4949 \| 0.7641 \| 0.7639 \| \| 0.4479 \| 22.99 \| 4000 \| 0.4966 \| 0.7630 \| 0.7629 \| \| 0.4468 \| 24.14 \| 4200 \| 0.4915 \| 0.7658 \| 0.7657 \| \| 0.4375 \| 25.29 \| 4400 \| 0.5056 \| 0.7644 \| 0.7639 \| \| 0.4442 \| 26.44 \| 4600 \| 0.4948 \| 0.7619 \| 0.7614 \| \| 0.4416 \| 27.59 \| 4800 \| 0.5015 \| 0.7672 \| 0.7668 \| \| 0.4381 \| 28.74 \| 5000 \| 0.4962 \| 0.7631 \| 0.7629 \| \| 0.4409 \| 29.89 \| 5200 \| 0.4953 \| 0.7659 \| 0.7654 \| \| 0.4345 \| 31.03 \| 5400 \| 0.4977 \| 0.7658 \| 0.7654 \| \| 0.4345 \| 32.18 \| 5600 \| 0.4902 \| 0.7655 \| 0.7654 \| \| 0.4294 \| 33.33 \| 5800 \| 0.5008 \| 0.7656 \| 0.7654 \| \| 0.4378 \| 34.48 \| 6000 \| 0.4893 \| 0.7666 \| 0.7661 \| \| 0.4267 \| 35.63 \| 6200 \| 0.4947 \| 0.7699 \| 0.7697 \| \| 0.434 \| 36.78 \| 6400 \| 0.4922 \| 0.7652 \| 0.7647 \| \| 0.4283 \| 37.93 \| 6600 \| 0.5046 \| 0.7654 \| 0.7650 \| \| 0.4271 \| 39.08 \| 6800 \| 0.4893 \| 0.7691 \| 0.7686 \| \| 0.4252 \| 40.23 \| 7000 \| 0.4951 \| 0.7623 \| 0.7618 \| \| 0.4233 \| 41.38 \| 7200 \| 0.4940 \| 0.7655 \| 0.7650 \| \| 0.425 \| 42.53 \| 7400 \| 0.4938 \| 0.7687 \| 0.7683 \| \| 0.426 \| 43.68 \| 7600 \| 0.4903 \| 0.7708 \| 0.7704 \| \| 0.4194 \| 44.83 \| 7800 \| 0.4950 \| 0.7648 \| 0.7643 \| \| 0.424 \| 45.98 \| 8000 \| 0.4897 \| 0.7694 \| 0.7690 \| \| 0.4236 \| 47.13 \| 8200 \| 0.4926 \| 0.7670 \| 0.7665 \| \| 0.4186 \| 48.28 \| 8400 \| 0.4926 \| 0.7669 \| 0.7665 \| \| 0.4177 \| 49.43 \| 8600 \| 0.4937 \| 0.7662 \| 0.7657 \| \| 0.4183 \| 50.57 \| 8800 \| 0.4941 \| 0.7669 \| 0.7665 \| \| 0.4197 \| 51.72 \| 9000 \| 0.4950 \| 0.7659 \| 0.7654 \| \| 0.4179 \| 52.87 \| 9200 \| 0.4951 \| 0.7655 \| 0.7650 \| \| 0.4188 \| 54.02 \| 9400 \| 0.4934 \| 0.7673 \| 0.7668 \| \| 0.4183 \| 55.17 \| 9600 \| 0.4939 \| 0.7673 \| 0.7668 \| \| 0.4171 \| 56.32 \| 9800 \| 0.4922 \| 0.7687 \| 0.7683 \| \| 0.4187 \| 57.47 \| 10000 \| 0.4928 \| 0.7684 \| 0.7679 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_EMP_H3K9ac-seqsight_4096_512_46M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K9ac-seqsight_4096_512_46M-L1_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_46M", "region:us" ]	null	2024-04-26T20:44:38+00:00	[]	[]	TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us	GUE\_EMP\_H3K9ac-seqsight\_4096\_512\_46M-L1\_f =============================================== This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_46M on the mahdibaghbanzadeh/GUE\_EMP\_H3K9ac dataset. It achieves the following results on the evaluation set: * Loss: 0.4618 * F1 Score: 0.8001 * Accuracy: 0.7996 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]	[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_46M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]
text2text-generation	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # UrduSum5k_multi2 This model is a fine-tuned version of [ahmed0189/mT5-Arabic-text-summarization](https://huggingface.co/ahmed0189/mT5-Arabic-text-summarization) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2260 - Rouge1: 0.14 - Rouge2: 0.0333 - Rougel: 0.14 - Rougelsum: 0.14 - Gen Len: 34.6705 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| 3.8803 \| 1.0 \| 1500 \| 3.2260 \| 0.14 \| 0.0333 \| 0.14 \| 0.14 \| 34.6705 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.13.3	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "UrduSum5k_multi2", "results": []}]}	Alefiah/UrduSum5k_multi2	null	[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-26T20:45:02+00:00	[]	[]	TAGS #transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	UrduSum5k\_multi2 ================= This model is a fine-tuned version of ahmed0189/mT5-Arabic-text-summarization on the None dataset. It achieves the following results on the evaluation set: * Loss: 3.2260 * Rouge1: 0.14 * Rouge2: 0.0333 * Rougel: 0.14 * Rougelsum: 0.14 * Gen Len: 34.6705 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 2 * eval\_batch\_size: 2 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 1 ### Training results ### Framework versions * Transformers 4.28.0 * Pytorch 2.2.1+cu121 * Datasets 2.19.0 * Tokenizers 0.13.3	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* Transformers 4.28.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.13.3" ]	[ "TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* Transformers 4.28.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.13.3" ]
reinforcement-learning	stable-baselines3	# PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) Follow to eval the agent locally: ```python repo_id = "Laz4rz/hf-LunarLander-1-ppo" # The repo_id filename = "ppo-LunarLander-v2.zip" # The model filename.zip checkpoint = load_from_hub(repo_id, filename) model = PPO.load(checkpoint) eval_env = Monitor(gym.make("LunarLander-v2")) mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True) print(f"mean_reward={mean_reward:.2f} +/- {std_reward}") ... ```	{"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "261.43 +/- 17.17", "name": "mean_reward", "verified": false}]}]}]}	Laz4rz/hf-LunarLander-1-ppo	null	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	null	2024-04-26T20:45:27+00:00	[]	[]	TAGS #stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us	# PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library. ## Usage (with Stable-baselines3) Follow to eval the agent locally:	[ "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nFollow to eval the agent locally:" ]	[ "TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nFollow to eval the agent locally:" ]
text-generation	null	# Phi-3-mini-4k-instruct - llamafile This repository contains executable weights (which we call [llamafiles](https://github.com/Mozilla-Ocho/llamafile)) that run on Linux, MacOS, Windows, FreeBSD, OpenBSD, and NetBSD for AMD64 and ARM64. - Model creator: [Microsoft](https://huggingface.co/microsoft) - Original model: [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) ## Quickstart Assuming your system has at least 32GB of RAM, you can try running the following command which download, concatenate, and execute the model. ``` wget https://huggingface.co/jartine/Phi-3-mini-4k-instruct-llamafile/resolve/main/Phi-3-mini-4k-instruct.F16.llamafile chmod +x Phi-3-mini-4k-instruct.F16.llamafile ./Phi-3-mini-4k-instruct.F16.llamafile --help # view manual ./Phi-3-mini-4k-instruct.F16.llamafile # launch web gui + oai api ./Phi-3-mini-4k-instruct.F16.llamafile -p ... # cli interface (scriptable) ``` Alternatively, you may download an official `llamafile` executable from Mozilla Ocho on GitHub, in which case you can use the Mixtral llamafiles as a simple weights data file. ``` llamafile -m ./Phi-3-mini-4k-instruct.F16.llamafile ... ``` For further information, please see the [llamafile README](https://github.com/mozilla-ocho/llamafile/). Having trouble? See the ["Gotchas" section](https://github.com/mozilla-ocho/llamafile/?tab=readme-ov-file#gotchas) of the README. ## Prompting Prompt template: ``` <\|system\|> You are a helpful AI assistant.<\|end\|> <\|user\|> How to explain Internet for a medieval knight?<\|end\|> <\|assistant\|> ``` Command template: ``` ./Phi-3-mini-4k-instruct.F16.llamafile -e -p "<\|user\|>\n{{prompt}}<\|end\|>\n<\|assistant\|>" ``` ## About llamafile llamafile is a new format introduced by Mozilla Ocho on Nov 20th 2023. It uses Cosmopolitan Libc to turn LLM weights into runnable llama.cpp binaries that run on the stock installs of six OSes for both ARM64 and AMD64. In addition to being executables, llamafiles are also zip archives. Each llamafile contains a GGUF file, which you can extract using the `unzip` command. If you want to change or add files to your llamafiles, then the `zipalign` command (distributed on the llamafile github) should be used instead of the traditional `zip` command. ## Licensing (Phi-3 Specific) The Phi-3 llamafiles are licensed Apache 2.0 because some of the software that went into creating these llamafiles uses that as its license. The Phi-3 weights themselves were published by Microsoft under the even more permissive MIT license. You can use the `unzip` command to extract the MIT-licensed GGUF file from each llamafile, which contains only the Microsoft Phi-3 weights. For further details on the complete picture, read our `LICENSE` file, since it documents the copyright notice of every transitive dependency. ## About Quantization Formats (General Advice) Your choice of quantization format depends on three things: 1. Will it fit in RAM or VRAM? 2. Is your use case reading (e.g. summarization) or writing (e.g. chatbot)? 3. llamafiles bigger than 4.30 GB are hard to run on Windows (see [gotchas](https://github.com/mozilla-ocho/llamafile/?tab=readme-ov-file#gotchas)) Good quants for writing (prediction speed) are Q5\_K\_M, and Q4\_0. Text generation is bounded by memory speed, so smaller quants help, but they cause the LLM to hallucinate more. However that doesn't mean they can't think correctly. A highly degraded quant like `Q2_K` may not make a great encyclopedia, but it's still capable of logical reasoning and the emergent capabilities LLMs exhibit. Good quants for reading (evaluation speed) are BF16, F16, Q8\_0, and Q4\_0 (ordered from fastest to slowest). Prompt evaluation is bounded by flop count, which means perf can be improved through software engineering alone, e.g. BLAS algorithms, in which case quantization starts hurting more than it helps, since it competes for CPU resources and makes it harder for the compiler to parallelize instructions. You want to ideally use the simplest smallest floating point format that's natively implemented by your hardware. In most cases, that's BF16 or FP16. However, llamafile is able to still offer respectable tinyBLAS speedups for llama.cpp's simplest quants: Q8\_0 and Q4\_0. -- ## Model Summary The Phi-3-Mini-4K-Instruct is a 3.8B parameters, lightweight, state-of-the-art open model trained with the Phi-3 datasets that includes both synthetic data and the filtered publicly available websites data with a focus on high-quality and reasoning dense properties. The model belongs to the Phi-3 family with the Mini version in two variants [4K](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) and [128K](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) which is the context length (in tokens) that it can support. The model has underwent a post-training process that incorporates both supervised fine-tuning and direct preference optimization for the instruction following and safety measures. When assessed against benchmarks testing common sense, language understanding, math, code, long context and logical reasoning, Phi-3 Mini-4K-Instruct showcased a robust and state-of-the-art performance among models with less than 13 billion parameters. Resources and Technical Documentation: + [Phi-3 Microsoft Blog](https://aka.ms/phi3blog-april) + [Phi-3 Technical Report](https://aka.ms/phi3-tech-report) + [Phi-3 on Azure AI Studio](https://aka.ms/phi3-azure-ai) + Phi-3 GGUF: [4K](https://aka.ms/Phi3-mini-4k-instruct-gguf) + Phi-3 ONNX: [4K](https://aka.ms/Phi3-mini-4k-instruct-onnx) ## Intended Uses Primary use cases The model is intended for commercial and research use in English. The model provides uses for applications which require: 1) Memory/compute constrained environments 2) Latency bound scenarios 3) Strong reasoning (especially code, math and logic) Our model is designed to accelerate research on language and multimodal models, for use as a building block for generative AI powered features. Use case considerations Our models are not specifically designed or evaluated for all downstream purposes. Developers should consider common limitations of language models as they select use cases, and evaluate and mitigate for accuracy, safety, and fariness before using within a specific downstream use case, particularly for high risk scenarios. Developers should be aware of and adhere to applicable laws or regulations (including privacy, trade compliance laws, etc.) that are relevant to their use case. Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under. ## How to Use Phi-3 Mini-4K-Instruct has been integrated in the development version (4.40.0) of `transformers`. Until the official version is released through `pip`, ensure that you are doing one of the following: * When loading the model, ensure that `trust_remote_code=True` is passed as an argument of the `from_pretrained()` function. * Update your local `transformers` to the development version: `pip uninstall -y transformers && pip install git+https://github.com/huggingface/transformers`. The previous command is an alternative to cloning and installing from the source. The current `transformers` version can be verified with: `pip list \| grep transformers`. Phi-3 Mini-4K-Instruct is also available in [HuggingChat](https://aka.ms/try-phi3-hf-chat). ### Tokenizer Phi-3 Mini-4K-Instruct supports a vocabulary size of up to `32064` tokens. The [tokenizer files](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/added_tokens.json) already provide placeholder tokens that can be used for downstream fine-tuning, but they can also be extended up to the model's vocabulary size. ### Chat Format Given the nature of the training data, the Phi-3 Mini-4K-Instruct model is best suited for prompts using the chat format as follows. You can provide the prompt as a question with a generic template as follow: ```markdown <\|user\|>\nQuestion <\|end\|>\n<\|assistant\|> ``` For example: ```markdown <\|system\|> You are a helpful AI assistant.<\|end\|> <\|user\|> How to explain Internet for a medieval knight?<\|end\|> <\|assistant\|> ``` where the model generates the text after `<\|assistant\|>` . In case of few-shots prompt, the prompt can be formatted as the following: ```markdown <\|system\|> You are a helpful AI assistant.<\|end\|> <\|user\|> I am going to Paris, what should I see?<\|end\|> <\|assistant\|> Paris, the capital of France, is known for its stunning architecture, art museums, historical landmarks, and romantic atmosphere. Here are some of the top attractions to see in Paris:\n\n1. The Eiffel Tower: The iconic Eiffel Tower is one of the most recognizable landmarks in the world and offers breathtaking views of the city.\n2. The Louvre Museum: The Louvre is one of the world's largest and most famous museums, housing an impressive collection of art and artifacts, including the Mona Lisa.\n3. Notre-Dame Cathedral: This beautiful cathedral is one of the most famous landmarks in Paris and is known for its Gothic architecture and stunning stained glass windows.\n\nThese are just a few of the many attractions that Paris has to offer. With so much to see and do, it's no wonder that Paris is one of the most popular tourist destinations in the world."<\|end\|> <\|user\|> What is so great about #1?<\|end\|> <\|assistant\|> ``` ### Sample inference code This code snippets show how to get quickly started with running the model on a GPU: ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline torch.random.manual_seed(0) model = AutoModelForCausalLM.from_pretrained( "microsoft/Phi-3-mini-4k-instruct", device_map="cuda", torch_dtype="auto", trust_remote_code=True, ) tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct") messages = [ {"role": "system", "content": "You are a helpful digital assistant. Please provide safe, ethical and accurate information to the user."}, {"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"}, {"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."}, {"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"}, ] pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, ) generation_args = { "max_new_tokens": 500, "return_full_text": False, "temperature": 0.0, "do_sample": False, } output = pipe(messages, *generation_args) print(output[0]['generated_text']) ``` Some applications/frameworks might not include a BOS token (`<s>`) at the start of the conversation. Please ensure that it is included since it provides more reliable results.* ## Responsible AI Considerations Like other language models, the Phi series models can potentially behave in ways that are unfair, unreliable, or offensive. Some of the limiting behaviors to be aware of include: + Quality of Service: the Phi models are trained primarily on English text. Languages other than English will experience worse performance. English language varieties with less representation in the training data might experience worse performance than standard American English. + Representation of Harms & Perpetuation of Stereotypes: These models can over- or under-represent groups of people, erase representation of some groups, or reinforce demeaning or negative stereotypes. Despite safety post-training, these limitations may still be present due to differing levels of representation of different groups or prevalence of examples of negative stereotypes in training data that reflect real-world patterns and societal biases. + Inappropriate or Offensive Content: these models may produce other types of inappropriate or offensive content, which may make it inappropriate to deploy for sensitive contexts without additional mitigations that are specific to the use case. + Information Reliability: Language models can generate nonsensical content or fabricate content that might sound reasonable but is inaccurate or outdated. + Limited Scope for Code: Majority of Phi-3 training data is based in Python and use common packages such as "typing, math, random, collections, datetime, itertools". If the model generates Python scripts that utilize other packages or scripts in other languages, we strongly recommend users manually verify all API uses. Developers should apply responsible AI best practices and are responsible for ensuring that a specific use case complies with relevant laws and regulations (e.g. privacy, trade, etc.). Important areas for consideration include: + Allocation: Models may not be suitable for scenarios that could have consequential impact on legal status or the allocation of resources or life opportunities (ex: housing, employment, credit, etc.) without further assessments and additional debiasing techniques. + High-Risk Scenarios: Developers should assess suitability of using models in high-risk scenarios where unfair, unreliable or offensive outputs might be extremely costly or lead to harm. This includes providing advice in sensitive or expert domains where accuracy and reliability are critical (ex: legal or health advice). Additional safeguards should be implemented at the application level according to the deployment context. + Misinformation: Models may produce inaccurate information. Developers should follow transparency best practices and inform end-users they are interacting with an AI system. At the application level, developers can build feedback mechanisms and pipelines to ground responses in use-case specific, contextual information, a technique known as Retrieval Augmented Generation (RAG). + Generation of Harmful Content: Developers should assess outputs for their context and use available safety classifiers or custom solutions appropriate for their use case. + Misuse: Other forms of misuse such as fraud, spam, or malware production may be possible, and developers should ensure that their applications do not violate applicable laws and regulations. ## Training ### Model * Architecture: Phi-3 Mini-4K-Instruct has 3.8B parameters and is a dense decoder-only Transformer model. The model is fine-tuned with Supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) to ensure alignment with human preferences and safety guidlines. * Inputs: Text. It is best suited for prompts using chat format. * Context length: 4K tokens * GPUs: 512 H100-80G * Training time: 7 days * Training data: 3.3T tokens * Outputs: Generated text in response to the input * Dates: Our models were trained between February and April 2024 * Status: This is a static model trained on an offline dataset with cutoff date October 2023. Future versions of the tuned models may be released as we improve models. ### Datasets Our training data includes a wide variety of sources, totaling 3.3 trillion tokens, and is a combination of 1) Publicly available documents filtered rigorously for quality, selected high-quality educational data, and code; 2) Newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (science, daily activities, theory of mind, etc.); 3) High quality chat format supervised data covering various topics to reflect human preferences on different aspects such as instruct-following, truthfulness, honesty and helpfulness. ### Fine-tuning A basic example of multi-GPUs supervised fine-tuning (SFT) with TRL and Accelerate modules is provided [here](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/sample_finetune.py). ## Benchmarks We report the results for Phi-3-Mini-4K-Instruct on standard open-source benchmarks measuring the model's reasoning ability (both common sense reasoning and logical reasoning). We compare to Phi-2, Mistral-7b-v0.1, Mixtral-8x7b, Gemma 7B, Llama-3-8B-Instruct, and GPT-3.5. All the reported numbers are produced with the exact same pipeline to ensure that the numbers are comparable. These numbers might differ from other published numbers due to slightly different choices in the evaluation. As is now standard, we use few-shot prompts to evaluate the models, at temperature 0. The prompts and number of shots are part of a Microsoft internal tool to evaluate language models, and in particular we did no optimization to the pipeline for Phi-3. More specifically, we do not change prompts, pick different few-shot examples, change prompt format, or do any other form of optimization for the model. The number of k–shot examples is listed per-benchmark. \| \| Phi-3-Mini-4K-In<br>3.8b \| Phi-3-Small<br>7b (preview) \| Phi-3-Medium<br>14b (preview) \| Phi-2<br>2.7b \| Mistral<br>7b \| Gemma<br>7b \| Llama-3-In<br>8b \| Mixtral<br>8x7b \| GPT-3.5<br>version 1106 \| \|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\| \| MMLU <br>5-Shot \| 68.8 \| 75.3 \| 78.2 \| 56.3 \| 61.7 \| 63.6 \| 66.5 \| 68.4 \| 71.4 \| \| HellaSwag <br> 5-Shot \| 76.7 \| 78.7 \| 83.2 \| 53.6 \| 58.5 \| 49.8 \| 71.1 \| 70.4 \| 78.8 \| \| ANLI <br> 7-Shot \| 52.8 \| 55.0 \| 58.7 \| 42.5 \| 47.1 \| 48.7 \| 57.3 \| 55.2 \| 58.1 \| \| GSM-8K <br> 0-Shot; CoT \| 82.5 \| 86.4 \| 90.8 \| 61.1 \| 46.4 \| 59.8 \| 77.4 \| 64.7 \| 78.1 \| \| MedQA <br> 2-Shot \| 53.8 \| 58.2 \| 69.8 \| 40.9 \| 49.6 \| 50.0 \| 60.5 \| 62.2 \| 63.4 \| \| AGIEval <br> 0-Shot \| 37.5 \| 45.0 \| 49.7 \| 29.8 \| 35.1 \| 42.1 \| 42.0 \| 45.2 \| 48.4 \| \| TriviaQA <br> 5-Shot \| 64.0 \| 59.1 \| 73.3 \| 45.2 \| 72.3 \| 75.2 \| 67.7 \| 82.2 \| 85.8 \| \| Arc-C <br> 10-Shot \| 84.9 \| 90.7 \| 91.9 \| 75.9 \| 78.6 \| 78.3 \| 82.8 \| 87.3 \| 87.4 \| \| Arc-E <br> 10-Shot \| 94.6 \| 97.1 \| 98.0 \| 88.5 \| 90.6 \| 91.4 \| 93.4 \| 95.6 \| 96.3 \| \| PIQA <br> 5-Shot \| 84.2 \| 87.8 \| 88.2 \| 60.2 \| 77.7 \| 78.1 \| 75.7 \| 86.0 \| 86.6 \| \| SociQA <br> 5-Shot \| 76.6 \| 79.0 \| 79.4 \| 68.3 \| 74.6 \| 65.5 \| 73.9 \| 75.9 \| 68.3 \| \| BigBench-Hard <br> 0-Shot \| 71.7 \| 75.0 \| 82.5 \| 59.4 \| 57.3 \| 59.6 \| 51.5 \| 69.7 \| 68.32 \| \| WinoGrande <br> 5-Shot \| 70.8 \| 82.5 \| 81.2 \| 54.7 \| 54.2 \| 55.6 \| 65 \| 62.0 \| 68.8 \| \| OpenBookQA <br> 10-Shot \| 83.2 \| 88.4 \| 86.6 \| 73.6 \| 79.8 \| 78.6 \| 82.6 \| 85.8 \| 86.0 \| \| BoolQ <br> 0-Shot \| 77.6 \| 82.9 \| 86.5 \| -- \| 72.2 \| 66.0 \| 80.9 \| 77.6 \| 79.1 \| \| CommonSenseQA <br> 10-Shot \| 80.2 \| 80.3 \| 82.6 \| 69.3 \| 72.6 \| 76.2 \| 79 \| 78.1 \| 79.6 \| \| TruthfulQA <br> 10-Shot \| 65.0 \| 68.1 \| 74.8 \| -- \| 52.1 \| 53.0 \| 63.2 \| 60.1 \| 85.8 \| \| HumanEval <br> 0-Shot \| 59.1 \| 59.1 \| 54.7 \| 47.0 \| 28.0 \| 34.1 \| 60.4 \| 37.8 \| 62.2 \| \| MBPP <br> 3-Shot \| 53.8 \| 71.4 \| 73.7 \| 60.6 \| 50.8 \| 51.5 \| 67.7 \| 60.2 \| 77.8 \| ## Software * [PyTorch](https://github.com/pytorch/pytorch) * [DeepSpeed](https://github.com/microsoft/DeepSpeed) * [Transformers](https://github.com/huggingface/transformers) * [Flash-Attention](https://github.com/HazyResearch/flash-attention) ## Hardware Note that by default, the Phi-3-mini model uses flash attention, which requires certain types of GPU hardware to run. We have tested on the following GPU types: * NVIDIA A100 * NVIDIA A6000 * NVIDIA H100 If you want to run the model on: * NVIDIA V100 or earlier generation GPUs: call AutoModelForCausalLM.from_pretrained() with attn_implementation="eager" * CPU: use the GGUF quantized models [4K](https://aka.ms/Phi3-mini-4k-instruct-gguf) + Optimized inference on GPU, CPU, and Mobile: use the ONNX models [4K](https://aka.ms/Phi3-mini-4k-instruct-onnx) ## Cross Platform Support ONNX runtime ecosystem now supports Phi-3 Mini models across platforms and hardware. You can find the optimized Phi-3 Mini-4K-Instruct ONNX model [here](https://aka.ms/phi3-mini-4k-instruct-onnx). Optimized Phi-3 models are also published here in ONNX format, to run with ONNX Runtime on CPU and GPU across devices, including server platforms, Windows, Linux and Mac desktops, and mobile CPUs, with the precision best suited to each of these targets. DirectML support lets developers bring hardware acceleration to Windows devices at scale across AMD, Intel, and NVIDIA GPUs. Along with DirectML, ONNX Runtime provides cross platform support for Phi-3 across a range of devices CPU, GPU, and mobile. Here are some of the optimized configurations we have added: 1. ONNX models for int4 DML: Quantized to int4 via AWQ 2. ONNX model for fp16 CUDA 3. ONNX model for int4 CUDA: Quantized to int4 via RTN 4. ONNX model for int4 CPU and Mobile: Quantized to int4 via RTN ## License The model is licensed under the [MIT license](https://huggingface.co/microsoft/Phi-3-mini-4k/resolve/main/LICENSE). ## Trademarks This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft’s Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies.	{"language": ["en"], "license": "apache-2.0", "tags": ["llamafile"], "base_model": "microsoft/Phi-3-mini-4k-instruct", "pipeline_tag": "text-generation", "prompt_template": "<\|system\|>\nYou are a helpful AI assistant.<\|end\|>\n<\|user\|>\n{{prompt}}<\|end\|>\n<\|assistant\|>"}	jartine/Phi-3-mini-4k-instruct-llamafile	null	[ "llamafile", "text-generation", "en", "base_model:microsoft/Phi-3-mini-4k-instruct", "license:apache-2.0", "region:us" ]	null	2024-04-26T20:47:56+00:00	[]	[ "en" ]	TAGS #llamafile #text-generation #en #base_model-microsoft/Phi-3-mini-4k-instruct #license-apache-2.0 #region-us	Phi-3-mini-4k-instruct - llamafile ================================== This repository contains executable weights (which we call llamafiles) that run on Linux, MacOS, Windows, FreeBSD, OpenBSD, and NetBSD for AMD64 and ARM64. * Model creator: Microsoft * Original model: microsoft/Phi-3-mini-4k-instruct Quickstart ---------- Assuming your system has at least 32GB of RAM, you can try running the following command which download, concatenate, and execute the model. Alternatively, you may download an official 'llamafile' executable from Mozilla Ocho on GitHub, in which case you can use the Mixtral llamafiles as a simple weights data file. For further information, please see the llamafile README. Having trouble? See the "Gotchas" section of the README. Prompting --------- Prompt template: Command template: About llamafile --------------- llamafile is a new format introduced by Mozilla Ocho on Nov 20th 2023. It uses Cosmopolitan Libc to turn LLM weights into runnable URL binaries that run on the stock installs of six OSes for both ARM64 and AMD64. In addition to being executables, llamafiles are also zip archives. Each llamafile contains a GGUF file, which you can extract using the 'unzip' command. If you want to change or add files to your llamafiles, then the 'zipalign' command (distributed on the llamafile github) should be used instead of the traditional 'zip' command. Licensing (Phi-3 Specific) -------------------------- The Phi-3 llamafiles are licensed Apache 2.0 because some of the software that went into creating these llamafiles uses that as its license. The Phi-3 weights themselves were published by Microsoft under the even more permissive MIT license. You can use the 'unzip' command to extract the MIT-licensed GGUF file from each llamafile, which contains only the Microsoft Phi-3 weights. For further details on the complete picture, read our 'LICENSE' file, since it documents the copyright notice of every transitive dependency. About Quantization Formats (General Advice) ------------------------------------------- Your choice of quantization format depends on three things: 1. Will it fit in RAM or VRAM? 2. Is your use case reading (e.g. summarization) or writing (e.g. chatbot)? 3. llamafiles bigger than 4.30 GB are hard to run on Windows (see gotchas) Good quants for writing (prediction speed) are Q5\_K\_M, and Q4\_0. Text generation is bounded by memory speed, so smaller quants help, but they cause the LLM to hallucinate more. However that doesn't mean they can't think correctly. A highly degraded quant like 'Q2\_K' may not make a great encyclopedia, but it's still capable of logical reasoning and the emergent capabilities LLMs exhibit. Good quants for reading (evaluation speed) are BF16, F16, Q8\_0, and Q4\_0 (ordered from fastest to slowest). Prompt evaluation is bounded by flop count, which means perf can be improved through software engineering alone, e.g. BLAS algorithms, in which case quantization starts hurting more than it helps, since it competes for CPU resources and makes it harder for the compiler to parallelize instructions. You want to ideally use the simplest smallest floating point format that's natively implemented by your hardware. In most cases, that's BF16 or FP16. However, llamafile is able to still offer respectable tinyBLAS speedups for URL's simplest quants: Q8\_0 and Q4\_0. -- Model Summary ------------- The Phi-3-Mini-4K-Instruct is a 3.8B parameters, lightweight, state-of-the-art open model trained with the Phi-3 datasets that includes both synthetic data and the filtered publicly available websites data with a focus on high-quality and reasoning dense properties. The model belongs to the Phi-3 family with the Mini version in two variants 4K and 128K which is the context length (in tokens) that it can support. The model has underwent a post-training process that incorporates both supervised fine-tuning and direct preference optimization for the instruction following and safety measures. When assessed against benchmarks testing common sense, language understanding, math, code, long context and logical reasoning, Phi-3 Mini-4K-Instruct showcased a robust and state-of-the-art performance among models with less than 13 billion parameters. Resources and Technical Documentation: * Phi-3 Microsoft Blog * Phi-3 Technical Report * Phi-3 on Azure AI Studio * Phi-3 GGUF: 4K * Phi-3 ONNX: 4K Intended Uses ------------- Primary use cases The model is intended for commercial and research use in English. The model provides uses for applications which require: 1. Memory/compute constrained environments 2. Latency bound scenarios 3. Strong reasoning (especially code, math and logic) Our model is designed to accelerate research on language and multimodal models, for use as a building block for generative AI powered features. Use case considerations Our models are not specifically designed or evaluated for all downstream purposes. Developers should consider common limitations of language models as they select use cases, and evaluate and mitigate for accuracy, safety, and fariness before using within a specific downstream use case, particularly for high risk scenarios. Developers should be aware of and adhere to applicable laws or regulations (including privacy, trade compliance laws, etc.) that are relevant to their use case. Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under. How to Use ---------- Phi-3 Mini-4K-Instruct has been integrated in the development version (4.40.0) of 'transformers'. Until the official version is released through 'pip', ensure that you are doing one of the following: * When loading the model, ensure that 'trust\_remote\_code=True' is passed as an argument of the 'from\_pretrained()' function. * Update your local 'transformers' to the development version: 'pip uninstall -y transformers && pip install git+URL The previous command is an alternative to cloning and installing from the source. The current 'transformers' version can be verified with: 'pip list \| grep transformers'. Phi-3 Mini-4K-Instruct is also available in HuggingChat. ### Tokenizer Phi-3 Mini-4K-Instruct supports a vocabulary size of up to '32064' tokens. The tokenizer files already provide placeholder tokens that can be used for downstream fine-tuning, but they can also be extended up to the model's vocabulary size. ### Chat Format Given the nature of the training data, the Phi-3 Mini-4K-Instruct model is best suited for prompts using the chat format as follows. You can provide the prompt as a question with a generic template as follow: For example: where the model generates the text after '<\|assistant\|>' . In case of few-shots prompt, the prompt can be formatted as the following: ### Sample inference code This code snippets show how to get quickly started with running the model on a GPU: Some applications/frameworks might not include a BOS token ('~~') at the start of the conversation. Please ensure that it is included since it provides more reliable results.~~ Responsible AI Considerations ----------------------------- Like other language models, the Phi series models can potentially behave in ways that are unfair, unreliable, or offensive. Some of the limiting behaviors to be aware of include: * Quality of Service: the Phi models are trained primarily on English text. Languages other than English will experience worse performance. English language varieties with less representation in the training data might experience worse performance than standard American English. * Representation of Harms & Perpetuation of Stereotypes: These models can over- or under-represent groups of people, erase representation of some groups, or reinforce demeaning or negative stereotypes. Despite safety post-training, these limitations may still be present due to differing levels of representation of different groups or prevalence of examples of negative stereotypes in training data that reflect real-world patterns and societal biases. * Inappropriate or Offensive Content: these models may produce other types of inappropriate or offensive content, which may make it inappropriate to deploy for sensitive contexts without additional mitigations that are specific to the use case. * Information Reliability: Language models can generate nonsensical content or fabricate content that might sound reasonable but is inaccurate or outdated. * Limited Scope for Code: Majority of Phi-3 training data is based in Python and use common packages such as "typing, math, random, collections, datetime, itertools". If the model generates Python scripts that utilize other packages or scripts in other languages, we strongly recommend users manually verify all API uses. Developers should apply responsible AI best practices and are responsible for ensuring that a specific use case complies with relevant laws and regulations (e.g. privacy, trade, etc.). Important areas for consideration include: * Allocation: Models may not be suitable for scenarios that could have consequential impact on legal status or the allocation of resources or life opportunities (ex: housing, employment, credit, etc.) without further assessments and additional debiasing techniques. * High-Risk Scenarios: Developers should assess suitability of using models in high-risk scenarios where unfair, unreliable or offensive outputs might be extremely costly or lead to harm. This includes providing advice in sensitive or expert domains where accuracy and reliability are critical (ex: legal or health advice). Additional safeguards should be implemented at the application level according to the deployment context. * Misinformation: Models may produce inaccurate information. Developers should follow transparency best practices and inform end-users they are interacting with an AI system. At the application level, developers can build feedback mechanisms and pipelines to ground responses in use-case specific, contextual information, a technique known as Retrieval Augmented Generation (RAG). * Generation of Harmful Content: Developers should assess outputs for their context and use available safety classifiers or custom solutions appropriate for their use case. * Misuse: Other forms of misuse such as fraud, spam, or malware production may be possible, and developers should ensure that their applications do not violate applicable laws and regulations. Training -------- ### Model * Architecture: Phi-3 Mini-4K-Instruct has 3.8B parameters and is a dense decoder-only Transformer model. The model is fine-tuned with Supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) to ensure alignment with human preferences and safety guidlines. * Inputs: Text. It is best suited for prompts using chat format. * Context length: 4K tokens * GPUs: 512 H100-80G * Training time: 7 days * Training data: 3.3T tokens * Outputs: Generated text in response to the input * Dates: Our models were trained between February and April 2024 * Status: This is a static model trained on an offline dataset with cutoff date October 2023. Future versions of the tuned models may be released as we improve models. ### Datasets Our training data includes a wide variety of sources, totaling 3.3 trillion tokens, and is a combination of 1. Publicly available documents filtered rigorously for quality, selected high-quality educational data, and code; 2. Newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (science, daily activities, theory of mind, etc.); 3. High quality chat format supervised data covering various topics to reflect human preferences on different aspects such as instruct-following, truthfulness, honesty and helpfulness. ### Fine-tuning A basic example of multi-GPUs supervised fine-tuning (SFT) with TRL and Accelerate modules is provided here. Benchmarks ---------- We report the results for Phi-3-Mini-4K-Instruct on standard open-source benchmarks measuring the model's reasoning ability (both common sense reasoning and logical reasoning). We compare to Phi-2, Mistral-7b-v0.1, Mixtral-8x7b, Gemma 7B, Llama-3-8B-Instruct, and GPT-3.5. All the reported numbers are produced with the exact same pipeline to ensure that the numbers are comparable. These numbers might differ from other published numbers due to slightly different choices in the evaluation. As is now standard, we use few-shot prompts to evaluate the models, at temperature 0. The prompts and number of shots are part of a Microsoft internal tool to evaluate language models, and in particular we did no optimization to the pipeline for Phi-3. More specifically, we do not change prompts, pick different few-shot examples, change prompt format, or do any other form of optimization for the model. The number of k–shot examples is listed per-benchmark. Software -------- * PyTorch * DeepSpeed * Transformers * Flash-Attention Hardware -------- Note that by default, the Phi-3-mini model uses flash attention, which requires certain types of GPU hardware to run. We have tested on the following GPU types: * NVIDIA A100 * NVIDIA A6000 * NVIDIA H100 If you want to run the model on: * NVIDIA V100 or earlier generation GPUs: call AutoModelForCausalLM.from\_pretrained() with attn\_implementation="eager" * CPU: use the GGUF quantized models 4K * Optimized inference on GPU, CPU, and Mobile: use the ONNX models 4K Cross Platform Support ---------------------- ONNX runtime ecosystem now supports Phi-3 Mini models across platforms and hardware. You can find the optimized Phi-3 Mini-4K-Instruct ONNX model here. Optimized Phi-3 models are also published here in ONNX format, to run with ONNX Runtime on CPU and GPU across devices, including server platforms, Windows, Linux and Mac desktops, and mobile CPUs, with the precision best suited to each of these targets. DirectML support lets developers bring hardware acceleration to Windows devices at scale across AMD, Intel, and NVIDIA GPUs. Along with DirectML, ONNX Runtime provides cross platform support for Phi-3 across a range of devices CPU, GPU, and mobile. Here are some of the optimized configurations we have added: 1. ONNX models for int4 DML: Quantized to int4 via AWQ 2. ONNX model for fp16 CUDA 3. ONNX model for int4 CUDA: Quantized to int4 via RTN 4. ONNX model for int4 CPU and Mobile: Quantized to int4 via RTN License ------- The model is licensed under the MIT license. Trademarks ---------- This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft’s Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies.	[ "### Tokenizer\n\n\nPhi-3 Mini-4K-Instruct supports a vocabulary size of up to '32064' tokens. The tokenizer files already provide placeholder tokens that can be used for downstream fine-tuning, but they can also be extended up to the model's vocabulary size.", "### Chat Format\n\n\nGiven the nature of the training data, the Phi-3 Mini-4K-Instruct model is best suited for prompts using the chat format as follows.\nYou can provide the prompt as a question with a generic template as follow:\n\n\nFor example:\n\n\nwhere the model generates the text after '<\|assistant\|>' . In case of few-shots prompt, the prompt can be formatted as the following:", "### Sample inference code\n\n\nThis code snippets show how to get quickly started with running the model on a GPU:\n\n\nSome applications/frameworks might not include a BOS token ('~~') at the start of the conversation. Please ensure that it is included since it provides more reliable results.~~\n\n\nResponsible AI Considerations\n-----------------------------\n\n\nLike other language models, the Phi series models can potentially behave in ways that are unfair, unreliable, or offensive. Some of the limiting behaviors to be aware of include:\n\n\n* Quality of Service: the Phi models are trained primarily on English text. Languages other than English will experience worse performance. English language varieties with less representation in the training data might experience worse performance than standard American English.\n* Representation of Harms & Perpetuation of Stereotypes: These models can over- or under-represent groups of people, erase representation of some groups, or reinforce demeaning or negative stereotypes. Despite safety post-training, these limitations may still be present due to differing levels of representation of different groups or prevalence of examples of negative stereotypes in training data that reflect real-world patterns and societal biases.\n* Inappropriate or Offensive Content: these models may produce other types of inappropriate or offensive content, which may make it inappropriate to deploy for sensitive contexts without additional mitigations that are specific to the use case.\n* Information Reliability: Language models can generate nonsensical content or fabricate content that might sound reasonable but is inaccurate or outdated.\n* Limited Scope for Code: Majority of Phi-3 training data is based in Python and use common packages such as \"typing, math, random, collections, datetime, itertools\". If the model generates Python scripts that utilize other packages or scripts in other languages, we strongly recommend users manually verify all API uses.\n\n\nDevelopers should apply responsible AI best practices and are responsible for ensuring that a specific use case complies with relevant laws and regulations (e.g. privacy, trade, etc.). Important areas for consideration include:\n\n\n* Allocation: Models may not be suitable for scenarios that could have consequential impact on legal status or the allocation of resources or life opportunities (ex: housing, employment, credit, etc.) without further assessments and additional debiasing techniques.\n* High-Risk Scenarios: Developers should assess suitability of using models in high-risk scenarios where unfair, unreliable or offensive outputs might be extremely costly or lead to harm. This includes providing advice in sensitive or expert domains where accuracy and reliability are critical (ex: legal or health advice). Additional safeguards should be implemented at the application level according to the deployment context.\n* Misinformation: Models may produce inaccurate information. Developers should follow transparency best practices and inform end-users they are interacting with an AI system. At the application level, developers can build feedback mechanisms and pipelines to ground responses in use-case specific, contextual information, a technique known as Retrieval Augmented Generation (RAG).\n* Generation of Harmful Content: Developers should assess outputs for their context and use available safety classifiers or custom solutions appropriate for their use case.\n* Misuse: Other forms of misuse such as fraud, spam, or malware production may be possible, and developers should ensure that their applications do not violate applicable laws and regulations.\n\n\nTraining\n--------", "### Model\n\n\n* Architecture: Phi-3 Mini-4K-Instruct has 3.8B parameters and is a dense decoder-only Transformer model. The model is fine-tuned with Supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) to ensure alignment with human preferences and safety guidlines.\n* Inputs: Text. It is best suited for prompts using chat format.\n* Context length: 4K tokens\n* GPUs: 512 H100-80G\n* Training time: 7 days\n* Training data: 3.3T tokens\n* Outputs: Generated text in response to the input\n* Dates: Our models were trained between February and April 2024\n* Status: This is a static model trained on an offline dataset with cutoff date October 2023. Future versions of the tuned models may be released as we improve models.", "### Datasets\n\n\nOur training data includes a wide variety of sources, totaling 3.3 trillion tokens, and is a combination of\n\n\n1. Publicly available documents filtered rigorously for quality, selected high-quality educational data, and code;\n2. Newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (science, daily activities, theory of mind, etc.);\n3. High quality chat format supervised data covering various topics to reflect human preferences on different aspects such as instruct-following, truthfulness, honesty and helpfulness.", "### Fine-tuning\n\n\nA basic example of multi-GPUs supervised fine-tuning (SFT) with TRL and Accelerate modules is provided here.\n\n\nBenchmarks\n----------\n\n\nWe report the results for Phi-3-Mini-4K-Instruct on standard open-source benchmarks measuring the model's reasoning ability (both common sense reasoning and logical reasoning). We compare to Phi-2, Mistral-7b-v0.1, Mixtral-8x7b, Gemma 7B, Llama-3-8B-Instruct, and GPT-3.5.\n\n\nAll the reported numbers are produced with the exact same pipeline to ensure that the numbers are comparable. These numbers might differ from other published numbers due to slightly different choices in the evaluation.\n\n\nAs is now standard, we use few-shot prompts to evaluate the models, at temperature 0.\nThe prompts and number of shots are part of a Microsoft internal tool to evaluate language models, and in particular we did no optimization to the pipeline for Phi-3.\nMore specifically, we do not change prompts, pick different few-shot examples, change prompt format, or do any other form of optimization for the model.\n\n\nThe number of k–shot examples is listed per-benchmark.\n\n\n\nSoftware\n--------\n\n\n* PyTorch\n* DeepSpeed\n* Transformers\n* Flash-Attention\n\n\nHardware\n--------\n\n\nNote that by default, the Phi-3-mini model uses flash attention, which requires certain types of GPU hardware to run. We have tested on the following GPU types:\n\n\n* NVIDIA A100\n* NVIDIA A6000\n* NVIDIA H100\n\n\nIf you want to run the model on:\n\n\n* NVIDIA V100 or earlier generation GPUs: call AutoModelForCausalLM.from\\_pretrained() with attn\\_implementation=\"eager\"\n* CPU: use the GGUF quantized models 4K\n\n\n* Optimized inference on GPU, CPU, and Mobile: use the ONNX models 4K\n\n\nCross Platform Support\n----------------------\n\n\nONNX runtime ecosystem now supports Phi-3 Mini models across platforms and hardware. You can find the optimized Phi-3 Mini-4K-Instruct ONNX model here.\n\n\nOptimized Phi-3 models are also published here in ONNX format, to run with ONNX Runtime on CPU and GPU across devices, including server platforms, Windows, Linux and Mac desktops, and mobile CPUs, with the precision best suited to each of these targets. DirectML support lets developers bring hardware acceleration to Windows devices at scale across AMD, Intel, and NVIDIA GPUs. \n\nAlong with DirectML, ONNX Runtime provides cross platform support for Phi-3 across a range of devices CPU, GPU, and mobile.\n\n\nHere are some of the optimized configurations we have added:\n\n\n1. ONNX models for int4 DML: Quantized to int4 via AWQ\n2. ONNX model for fp16 CUDA\n3. ONNX model for int4 CUDA: Quantized to int4 via RTN\n4. ONNX model for int4 CPU and Mobile: Quantized to int4 via RTN\n\n\nLicense\n-------\n\n\nThe model is licensed under the MIT license.\n\n\nTrademarks\n----------\n\n\nThis project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft’s Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies." ]	[ "TAGS\n#llamafile #text-generation #en #base_model-microsoft/Phi-3-mini-4k-instruct #license-apache-2.0 #region-us \n", "### Tokenizer\n\n\nPhi-3 Mini-4K-Instruct supports a vocabulary size of up to '32064' tokens. The tokenizer files already provide placeholder tokens that can be used for downstream fine-tuning, but they can also be extended up to the model's vocabulary size.", "### Chat Format\n\n\nGiven the nature of the training data, the Phi-3 Mini-4K-Instruct model is best suited for prompts using the chat format as follows.\nYou can provide the prompt as a question with a generic template as follow:\n\n\nFor example:\n\n\nwhere the model generates the text after '<\|assistant\|>' . In case of few-shots prompt, the prompt can be formatted as the following:", "### Sample inference code\n\n\nThis code snippets show how to get quickly started with running the model on a GPU:\n\n\nSome applications/frameworks might not include a BOS token ('~~') at the start of the conversation. Please ensure that it is included since it provides more reliable results.~~\n\n\nResponsible AI Considerations\n-----------------------------\n\n\nLike other language models, the Phi series models can potentially behave in ways that are unfair, unreliable, or offensive. Some of the limiting behaviors to be aware of include:\n\n\n* Quality of Service: the Phi models are trained primarily on English text. Languages other than English will experience worse performance. English language varieties with less representation in the training data might experience worse performance than standard American English.\n* Representation of Harms & Perpetuation of Stereotypes: These models can over- or under-represent groups of people, erase representation of some groups, or reinforce demeaning or negative stereotypes. Despite safety post-training, these limitations may still be present due to differing levels of representation of different groups or prevalence of examples of negative stereotypes in training data that reflect real-world patterns and societal biases.\n* Inappropriate or Offensive Content: these models may produce other types of inappropriate or offensive content, which may make it inappropriate to deploy for sensitive contexts without additional mitigations that are specific to the use case.\n* Information Reliability: Language models can generate nonsensical content or fabricate content that might sound reasonable but is inaccurate or outdated.\n* Limited Scope for Code: Majority of Phi-3 training data is based in Python and use common packages such as \"typing, math, random, collections, datetime, itertools\". If the model generates Python scripts that utilize other packages or scripts in other languages, we strongly recommend users manually verify all API uses.\n\n\nDevelopers should apply responsible AI best practices and are responsible for ensuring that a specific use case complies with relevant laws and regulations (e.g. privacy, trade, etc.). Important areas for consideration include:\n\n\n* Allocation: Models may not be suitable for scenarios that could have consequential impact on legal status or the allocation of resources or life opportunities (ex: housing, employment, credit, etc.) without further assessments and additional debiasing techniques.\n* High-Risk Scenarios: Developers should assess suitability of using models in high-risk scenarios where unfair, unreliable or offensive outputs might be extremely costly or lead to harm. This includes providing advice in sensitive or expert domains where accuracy and reliability are critical (ex: legal or health advice). Additional safeguards should be implemented at the application level according to the deployment context.\n* Misinformation: Models may produce inaccurate information. Developers should follow transparency best practices and inform end-users they are interacting with an AI system. At the application level, developers can build feedback mechanisms and pipelines to ground responses in use-case specific, contextual information, a technique known as Retrieval Augmented Generation (RAG).\n* Generation of Harmful Content: Developers should assess outputs for their context and use available safety classifiers or custom solutions appropriate for their use case.\n* Misuse: Other forms of misuse such as fraud, spam, or malware production may be possible, and developers should ensure that their applications do not violate applicable laws and regulations.\n\n\nTraining\n--------", "### Model\n\n\n* Architecture: Phi-3 Mini-4K-Instruct has 3.8B parameters and is a dense decoder-only Transformer model. The model is fine-tuned with Supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) to ensure alignment with human preferences and safety guidlines.\n* Inputs: Text. It is best suited for prompts using chat format.\n* Context length: 4K tokens\n* GPUs: 512 H100-80G\n* Training time: 7 days\n* Training data: 3.3T tokens\n* Outputs: Generated text in response to the input\n* Dates: Our models were trained between February and April 2024\n* Status: This is a static model trained on an offline dataset with cutoff date October 2023. Future versions of the tuned models may be released as we improve models.", "### Datasets\n\n\nOur training data includes a wide variety of sources, totaling 3.3 trillion tokens, and is a combination of\n\n\n1. Publicly available documents filtered rigorously for quality, selected high-quality educational data, and code;\n2. Newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (science, daily activities, theory of mind, etc.);\n3. High quality chat format supervised data covering various topics to reflect human preferences on different aspects such as instruct-following, truthfulness, honesty and helpfulness.", "### Fine-tuning\n\n\nA basic example of multi-GPUs supervised fine-tuning (SFT) with TRL and Accelerate modules is provided here.\n\n\nBenchmarks\n----------\n\n\nWe report the results for Phi-3-Mini-4K-Instruct on standard open-source benchmarks measuring the model's reasoning ability (both common sense reasoning and logical reasoning). We compare to Phi-2, Mistral-7b-v0.1, Mixtral-8x7b, Gemma 7B, Llama-3-8B-Instruct, and GPT-3.5.\n\n\nAll the reported numbers are produced with the exact same pipeline to ensure that the numbers are comparable. These numbers might differ from other published numbers due to slightly different choices in the evaluation.\n\n\nAs is now standard, we use few-shot prompts to evaluate the models, at temperature 0.\nThe prompts and number of shots are part of a Microsoft internal tool to evaluate language models, and in particular we did no optimization to the pipeline for Phi-3.\nMore specifically, we do not change prompts, pick different few-shot examples, change prompt format, or do any other form of optimization for the model.\n\n\nThe number of k–shot examples is listed per-benchmark.\n\n\n\nSoftware\n--------\n\n\n* PyTorch\n* DeepSpeed\n* Transformers\n* Flash-Attention\n\n\nHardware\n--------\n\n\nNote that by default, the Phi-3-mini model uses flash attention, which requires certain types of GPU hardware to run. We have tested on the following GPU types:\n\n\n* NVIDIA A100\n* NVIDIA A6000\n* NVIDIA H100\n\n\nIf you want to run the model on:\n\n\n* NVIDIA V100 or earlier generation GPUs: call AutoModelForCausalLM.from\\_pretrained() with attn\\_implementation=\"eager\"\n* CPU: use the GGUF quantized models 4K\n\n\n* Optimized inference on GPU, CPU, and Mobile: use the ONNX models 4K\n\n\nCross Platform Support\n----------------------\n\n\nONNX runtime ecosystem now supports Phi-3 Mini models across platforms and hardware. You can find the optimized Phi-3 Mini-4K-Instruct ONNX model here.\n\n\nOptimized Phi-3 models are also published here in ONNX format, to run with ONNX Runtime on CPU and GPU across devices, including server platforms, Windows, Linux and Mac desktops, and mobile CPUs, with the precision best suited to each of these targets. DirectML support lets developers bring hardware acceleration to Windows devices at scale across AMD, Intel, and NVIDIA GPUs. \n\nAlong with DirectML, ONNX Runtime provides cross platform support for Phi-3 across a range of devices CPU, GPU, and mobile.\n\n\nHere are some of the optimized configurations we have added:\n\n\n1. ONNX models for int4 DML: Quantized to int4 via AWQ\n2. ONNX model for fp16 CUDA\n3. ONNX model for int4 CUDA: Quantized to int4 via RTN\n4. ONNX model for int4 CPU and Mobile: Quantized to int4 via RTN\n\n\nLicense\n-------\n\n\nThe model is licensed under the MIT license.\n\n\nTrademarks\n----------\n\n\nThis project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft’s Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies." ]
text-generation	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_eli5_clm-model This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the eli5_category dataset. It achieves the following results on the evaluation set: - Loss: 3.5790 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.6908 \| 1.0 \| 1273 \| 3.5831 \| \| 3.5749 \| 2.0 \| 2546 \| 3.5787 \| \| 3.5283 \| 3.0 \| 3819 \| 3.5790 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["eli5_category"], "base_model": "gpt2", "model-index": [{"name": "my_awesome_eli5_clm-model", "results": []}]}	GauravR12060102/my_awesome_eli5_clm-model	null	[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "dataset:eli5_category", "base_model:gpt2", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-26T20:48:38+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #dataset-eli5_category #base_model-gpt2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	my\_awesome\_eli5\_clm-model ============================ This model is a fine-tuned version of gpt2 on the eli5\_category dataset. It achieves the following results on the evaluation set: * Loss: 3.5790 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3.0 ### Training results ### Framework versions * Transformers 4.40.1 * Pytorch 2.2.1+cu121 * Datasets 2.19.0 * Tokenizers 0.19.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.1\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #dataset-eli5_category #base_model-gpt2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.1\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]
null	transformers	## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/HPAI-BSC/Llama3-Aloe-8B-Alpha <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Llama3-Aloe-8B-Alpha-GGUF/resolve/main/Llama3-Aloe-8B-Alpha.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama3-Aloe-8B-Alpha-GGUF/resolve/main/Llama3-Aloe-8B-Alpha.IQ3_XS.gguf) \| IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama3-Aloe-8B-Alpha-GGUF/resolve/main/Llama3-Aloe-8B-Alpha.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama3-Aloe-8B-Alpha-GGUF/resolve/main/Llama3-Aloe-8B-Alpha.IQ3_S.gguf) \| IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Llama3-Aloe-8B-Alpha-GGUF/resolve/main/Llama3-Aloe-8B-Alpha.IQ3_M.gguf) \| IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama3-Aloe-8B-Alpha-GGUF/resolve/main/Llama3-Aloe-8B-Alpha.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama3-Aloe-8B-Alpha-GGUF/resolve/main/Llama3-Aloe-8B-Alpha.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama3-Aloe-8B-Alpha-GGUF/resolve/main/Llama3-Aloe-8B-Alpha.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama3-Aloe-8B-Alpha-GGUF/resolve/main/Llama3-Aloe-8B-Alpha.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama3-Aloe-8B-Alpha-GGUF/resolve/main/Llama3-Aloe-8B-Alpha.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama3-Aloe-8B-Alpha-GGUF/resolve/main/Llama3-Aloe-8B-Alpha.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama3-Aloe-8B-Alpha-GGUF/resolve/main/Llama3-Aloe-8B-Alpha.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama3-Aloe-8B-Alpha-GGUF/resolve/main/Llama3-Aloe-8B-Alpha.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama3-Aloe-8B-Alpha-GGUF/resolve/main/Llama3-Aloe-8B-Alpha.Q8_0.gguf) \| Q8_0 \| 8.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama3-Aloe-8B-Alpha-GGUF/resolve/main/Llama3-Aloe-8B-Alpha.f16.gguf) \| f16 \| 16.2 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->	{"language": ["en"], "license": "cc-by-nc-4.0", "library_name": "transformers", "tags": ["biology", "medical"], "datasets": ["argilla/dpo-mix-7k", "nvidia/HelpSteer", "jondurbin/airoboros-3.2", "hkust-nlp/deita-10k-v0", "LDJnr/Capybara", "HPAI-BSC/CareQA", "GBaker/MedQA-USMLE-4-options", "lukaemon/mmlu", "bigbio/pubmed_qa", "openlifescienceai/medmcqa", "bigbio/med_qa"], "base_model": "HPAI-BSC/Llama3-Aloe-8B-Alpha", "quantized_by": "mradermacher"}	mradermacher/Llama3-Aloe-8B-Alpha-GGUF	null	[ "transformers", "gguf", "biology", "medical", "en", "dataset:argilla/dpo-mix-7k", "dataset:nvidia/HelpSteer", "dataset:jondurbin/airoboros-3.2", "dataset:hkust-nlp/deita-10k-v0", "dataset:LDJnr/Capybara", "dataset:HPAI-BSC/CareQA", "dataset:GBaker/MedQA-USMLE-4-options", "dataset:lukaemon/mmlu", "dataset:bigbio/pubmed_qa", "dataset:openlifescienceai/medmcqa", "dataset:bigbio/med_qa", "base_model:HPAI-BSC/Llama3-Aloe-8B-Alpha", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	null	2024-04-26T20:49:47+00:00	[]	[ "en" ]	TAGS #transformers #gguf #biology #medical #en #dataset-argilla/dpo-mix-7k #dataset-nvidia/HelpSteer #dataset-jondurbin/airoboros-3.2 #dataset-hkust-nlp/deita-10k-v0 #dataset-LDJnr/Capybara #dataset-HPAI-BSC/CareQA #dataset-GBaker/MedQA-USMLE-4-options #dataset-lukaemon/mmlu #dataset-bigbio/pubmed_qa #dataset-openlifescienceai/medmcqa #dataset-bigbio/med_qa #base_model-HPAI-BSC/Llama3-Aloe-8B-Alpha #license-cc-by-nc-4.0 #endpoints_compatible #region-us	About ----- static quants of URL weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. Usage ----- If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. Provided Quants --------------- (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): !URL And here are Artefact2's thoughts on the matter: URL FAQ / Model Request ------------------- See URL for some answers to questions you might have and/or if you want some other model quantized. Thanks ------ I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.	[]	[ "TAGS\n#transformers #gguf #biology #medical #en #dataset-argilla/dpo-mix-7k #dataset-nvidia/HelpSteer #dataset-jondurbin/airoboros-3.2 #dataset-hkust-nlp/deita-10k-v0 #dataset-LDJnr/Capybara #dataset-HPAI-BSC/CareQA #dataset-GBaker/MedQA-USMLE-4-options #dataset-lukaemon/mmlu #dataset-bigbio/pubmed_qa #dataset-openlifescienceai/medmcqa #dataset-bigbio/med_qa #base_model-HPAI-BSC/Llama3-Aloe-8B-Alpha #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n" ]
text-generation	transformers	[Phi 3 128K instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) weights in the Llama format. All credit goes to @vonjack for their useful [conversion script](https://huggingface.co/vonjack/Phi-3-mini-4k-instruct-LLaMAfied) as well as index files which were used to make this. Check out their version for the [4k model](https://huggingface.co/vonjack/Phi-3-mini-4k-instruct-LLaMAfied)!	{"language": ["en"], "license": "mit"}	mrfakename/llamaphi-3-128k-instruct	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "en", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-26T20:52:04+00:00	[]	[ "en" ]	TAGS #transformers #safetensors #llama #text-generation #conversational #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Phi 3 128K instruct weights in the Llama format. All credit goes to @vonjack for their useful conversion script as well as index files which were used to make this. Check out their version for the 4k model!	[]	[ "TAGS\n#transformers #safetensors #llama #text-generation #conversational #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
null	null	<div align="center"> <img width="260px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/BrQCb95lmEIFz79QAmoNA.png"></div> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/fJIOPJnY6Ff6fUiSIuMEt.png) <div align="center"> <h1>Advancing Open-source Large Language Models in Medical Domain</h1> </div> <p align="center" style="margin-top: 0px;"> <a href="https://colab.research.google.com/drive/1F5oV20InEYeAJGmBwYF9NM_QhLmjBkKJ?usp=sharing"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="OpenChat Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 10px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">Online Demo</span> </a> \| <a href="https://github.com/openlifescience-ai"> <img src="https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png" alt="GitHub Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">GitHub</span> </a> \| <a href="#"> <img src="https://github.com/alpayariyak/openchat/blob/master/assets/arxiv-logomark-small-square-border.png?raw=true" alt="ArXiv Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style="margin-right: 5px;">Paper</span> </a> \| <a href="https://discord.gg/A5Fjf5zC69"> <img src="https://cloud.githubusercontent.com/assets/6291467/26705903/96c2d66e-477c-11e7-9f4e-f3c0efe96c9a.png" alt="Discord Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text">Discord</span> </a> </p> ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/KGmRE5w2sepNtwsEu8t7K.jpeg) Introducing OpenBioLLM-70B: A State-of-the-Art Open Source Biomedical Large Language Model OpenBioLLM-70B is an advanced open source language model designed specifically for the biomedical domain. Developed by Saama AI Labs, this model leverages cutting-edge techniques to achieve state-of-the-art performance on a wide range of biomedical tasks. 🏥 Biomedical Specialization: OpenBioLLM-70B is tailored for the unique language and knowledge requirements of the medical and life sciences fields. It was fine-tuned on a vast corpus of high-quality biomedical data, enabling it to understand and generate text with domain-specific accuracy and fluency. 🎓 Superior Performance: With 70 billion parameters, OpenBioLLM-70B outperforms other open source biomedical language models of similar scale. It has also demonstrated better results compared to larger proprietary & open-source models like GPT-4, Gemini, Meditron-70B, Med-PaLM-1 & Med-PaLM-2 on biomedical benchmarks. 🧠 Advanced Training Techniques: OpenBioLLM-70B builds upon the powerful foundations of the Meta-Llama-3-70B-Instruct and [Meta-Llama-3-70B-Instruct](meta-llama/Meta-Llama-3-70B-Instruct) models. It incorporates the DPO dataset and fine-tuning recipe along with a custom diverse medical instruction dataset. Key components of the training pipeline include: <div align="center"> <img width="1200px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/oPchsJsEpQoGcGXVbh7YS.png"> </div> - Policy Optimization: [Direct Preference Optimization: Your Language Model is Secretly a Reward Model (DPO)](https://arxiv.org/abs/2305.18290) - Fine-tuning dataset: Custom Medical Instruct dataset (We plan to release a sample training dataset in our upcoming paper; please stay updated) This combination of cutting-edge techniques enables OpenBioLLM-70B to align with key capabilities and preferences for biomedical applications. ⚙️ Release Details: - Model Size: 70 billion parameters - Quantization: Optimized quantized versions available [Here](https://huggingface.co/aaditya/OpenBioLLM-70B-GGUF) - Language(s) (NLP): en - Developed By: [Ankit Pal (Aaditya Ura)](https://aadityaura.github.io/) from Saama AI Labs - License: Meta-Llama License - Fine-tuned from models: [Meta-Llama-3-70B-Instruct](meta-llama/Meta-Llama-3-70B-Instruct) - Resources for more information: - Paper: Coming soon The model can be fine-tuned for more specialized tasks and datasets as needed. OpenBioLLM-70B represents an important step forward in democratizing advanced language AI for the biomedical community. By leveraging state-of-the-art architectures and training techniques from leading open source efforts like Llama-3, we have created a powerful tool to accelerate innovation and discovery in healthcare and the life sciences. We are excited to share OpenBioLLM-70B with researchers and developers around the world. ### Use with transformers Important: Please use the exact chat template provided by Llama-3 instruct version. Otherwise there will be a degradation in the performance. The model output can be verbose in rare cases. Please consider setting temperature = 0 to make this happen less. See the snippet below for usage with Transformers: ```python import transformers import torch model_id = "aaditya/OpenBioLLM-Llama3-70B" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device="auto", ) messages = [ {"role": "system", "content": "You are an expert and experienced from the healthcare and biomedical domain with extensive medical knowledge and practical experience. Your name is OpenBioLLM, and you were developed by Saama AI Labs. who's willing to help answer the user's query with explanation. In your explanation, leverage your deep medical expertise such as relevant anatomical structures, physiological processes, diagnostic criteria, treatment guidelines, or other pertinent medical concepts. Use precise medical terminology while still aiming to make the explanation clear and accessible to a general audience."}, {"role": "user", "content": "How can i split a 3mg or 4mg waefin pill so i can get a 2.5mg pill?"}, ] prompt = pipeline.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) terminators = [ pipeline.tokenizer.eos_token_id, pipeline.tokenizer.convert_tokens_to_ids("<\|eot_id\|>") ] outputs = pipeline( prompt, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.0, top_p=0.9, ) print(outputs[0]["generated_text"][len(prompt):]) ``` ## Training procedure ### Training hyperparameters <details> <summary>Click to see details</summary> - learning_rate: 0.0002 - lr_scheduler: cosine - train_batch_size: 12 - eval_batch_size: 8 - GPU: H100 80GB SXM5 - num_devices: 8 - optimizer: adamw_bnb_8bit - lr_scheduler_warmup_steps: 100 - num_epochs: 4 </details> ### Peft hyperparameters <details> <summary>Click to see details</summary> - adapter: qlora - lora_r: 128 - lora_alpha: 256 - lora_dropout: 0.05 - lora_target_linear: true -lora_target_modules: - q_proj - v_proj - k_proj - o_proj - gate_proj - down_proj - up_proj </details> ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.1 - Axolotl - Lm harness for evaluation # Benchmark Results 🔥 OpenBioLLM-70B demonstrates superior performance compared to larger models, such as GPT-4, Gemini, Meditron-70B, Med-PaLM-1 & Med-PaLM-2 across 9 diverse biomedical datasets, achieving state-of-the-art results with an average score of 86.06%, despite having a significantly smaller parameter count. The model's strong performance in domain-specific tasks, such as Clinical KG, Medical Genetics, and PubMedQA, highlights its ability to effectively capture and apply biomedical knowledge. 🚨 The GPT-4, Med-PaLM-1, and Med-PaLM-2 results are taken from their official papers. Since Med-PaLM doesn't provide zero-shot accuracy, we are using 5-shot accuracy from their paper for comparison. All results presented are in the zero-shot setting, except for Med-PaLM-2 and Med-PaLM-1, which use 5-shot accuracy. \| \| Clinical KG \| Medical Genetics \| Anatomy \| Pro Medicine \| College Biology \| College Medicine \| MedQA 4 opts \| PubMedQA \| MedMCQA \| Avg \| \|--------------------\|-------------\|------------------\|---------\|--------------\|-----------------\|------------------\|--------------\|----------\|---------\|-------\| \| OpenBioLLM-70B \| 92.93 \| 93.197 \| 83.904 \| 93.75 \| 93.827 \| 85.749 \| 78.162 \| 78.97 \| 74.014 \| 86.05588 \| \| Med-PaLM-2 (5-shot) \| 88.3 \| 90 \| 77.8 \| 95.2 \| 94.4 \| 80.9 \| 79.7 \| 79.2 \| 71.3 \| 84.08 \| \| GPT-4 \| 86.04 \| 91 \| 80 \| 93.01 \| 95.14 \| 76.88 \| 78.87 \| 75.2 \| 69.52 \| 82.85 \| \| Med-PaLM-1 (Flan-PaLM, 5-shot) \| 80.4 \| 75 \| 63.7 \| 83.8 \| 88.9 \| 76.3 \| 67.6 \| 79 \| 57.6 \| 74.7 \| \| OpenBioLLM-8B \| 76.101 \| 86.1 \| 69.829 \| 78.21 \| 84.213 \| 68.042 \| 58.993 \| 74.12 \| 56.913 \| 72.502 \| \| Gemini-1.0 \| 76.7 \| 75.8 \| 66.7 \| 77.7 \| 88 \| 69.2 \| 58 \| 70.7 \| 54.3 \| 70.79 \| \| GPT-3.5 Turbo 1106 \| 74.71 \| 74 \| 72.79 \| 72.79 \| 72.91 \| 64.73 \| 57.71 \| 72.66 \| 53.79 \| 66 \| \| Meditron-70B \| 66.79 \| 69 \| 53.33 \| 71.69 \| 76.38 \| 63 \| 57.1 \| 76.6 \| 46.85 \| 64.52 \| \| gemma-7b \| 69.81 \| 70 \| 59.26 \| 66.18 \| 79.86 \| 60.12 \| 47.21 \| 76.2 \| 48.96 \| 64.18 \| \| Mistral-7B-v0.1 \| 68.68 \| 71 \| 55.56 \| 68.38 \| 68.06 \| 59.54 \| 50.82 \| 75.4 \| 48.2 \| 62.85 \| \| Apollo-7B \| 62.26 \| 72 \| 61.48 \| 69.12 \| 70.83 \| 55.49 \| 55.22 \| 39.8 \| 53.77 \| 60 \| \| MedAlpaca-7b \| 57.36 \| 69 \| 57.04 \| 67.28 \| 65.28 \| 54.34 \| 41.71 \| 72.8 \| 37.51 \| 58.03 \| \| BioMistral-7B \| 59.9 \| 64 \| 56.5 \| 60.4 \| 59 \| 54.7 \| 50.6 \| 77.5 \| 48.1 \| 57.3 \| \| AlpaCare-llama2-7b \| 49.81 \| 49 \| 45.92 \| 33.82 \| 50 \| 43.35 \| 29.77 \| 72.2 \| 34.42 \| 45.36 \| \| ClinicalGPT \| 30.56 \| 27 \| 30.37 \| 19.48 \| 25 \| 24.27 \| 26.08 \| 63.8 \| 28.18 \| 30.52 \| <div align="center"> <img width="1600px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/_SzdcJSBjZyo8RS1bTEkP.png"> </div> ## Detailed Medical Subjectwise accuracy ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/UXF-V0col0Z0sS6BGPBkE.png) # Use Cases & Examples 🚨 Below results are from the quantized version of OpenBioLLM-70B # Summarize Clinical Notes OpenBioLLM-70B can efficiently analyze and summarize complex clinical notes, EHR data, and discharge summaries, extracting key information and generating concise, structured summaries ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/xdwdBgOxNi_TfML0hKlI8.png) # Answer Medical Questions OpenBioLLM-70B can provide answers to a wide range of medical questions. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/zO95GlwOQEZqCKQF69mE6.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/OKBczKw7gWeW5xsuDpc27.png) <details> <summary>Click to see details</summary> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/eJGHT5khppYvJb8fQ-YW4.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/Cnbwrqa_-ORHRuNRC2P6Y.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/J9DhdcvukAc9mnnW9fj2C.png) </details> # Clinical Entity Recognition OpenBioLLM-70B can perform advanced clinical entity recognition by identifying and extracting key medical concepts, such as diseases, symptoms, medications, procedures, and anatomical structures, from unstructured clinical text. By leveraging its deep understanding of medical terminology and context, the model can accurately annotate and categorize clinical entities, enabling more efficient information retrieval, data analysis, and knowledge discovery from electronic health records, research articles, and other biomedical text sources. This capability can support various downstream applications, such as clinical decision support, pharmacovigilance, and medical research. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/_69BW4k9LVABFwtxixL45.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/DKy5wYCoPhoPPUc1-x8_J.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/7WD9zCCBZT4-4XlfnIQjl.png) # Biomarkers Extraction ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/ZttoM4AiteT7gFYVhjIpN.png) # Classification OpenBioLLM-70B can perform various biomedical classification tasks, such as disease prediction, sentiment analysis, medical document categorization ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/Bf5MW1d75qT-1F_TR_hC0.png) # De-Identification OpenBioLLM-70B can detect and remove personally identifiable information (PII) from medical records, ensuring patient privacy and compliance with data protection regulations like HIPAA. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/hKX4kzm--Tw5bj6K78msy.png) Advisory Notice! While OpenBioLLM-70B leverages high-quality data sources, its outputs may still contain inaccuracies, biases, or misalignments that could pose risks if relied upon for medical decision-making without further testing and refinement. The model's performance has not yet been rigorously evaluated in randomized controlled trials or real-world healthcare environments. Therefore, we strongly advise against using OpenBioLLM-70B for any direct patient care, clinical decision support, or other professional medical purposes at this time. Its use should be limited to research, development, and exploratory applications by qualified individuals who understand its limitations. OpenBioLLM-70B is intended solely as a research tool to assist healthcare professionals and should never be considered a replacement for the professional judgment and expertise of a qualified medical doctor. Appropriately adapting and validating OpenBioLLM-70B for specific medical use cases would require significant additional work, potentially including: - Thorough testing and evaluation in relevant clinical scenarios - Alignment with evidence-based guidelines and best practices - Mitigation of potential biases and failure modes - Integration with human oversight and interpretation - Compliance with regulatory and ethical standards Always consult a qualified healthcare provider for personal medical needs. # Citation If you find OpenBioLLM-70B & 8B useful in your work, please cite the model as follows: ``` @misc{OpenBioLLMs, author = {Ankit Pal, Malaikannan Sankarasubbu}, title = {OpenBioLLMs: Advancing Open-Source Large Language Models for Healthcare and Life Sciences}, year = {2024}, publisher = {Hugging Face}, journal = {Hugging Face repository}, howpublished = {\url{https://huggingface.co/aaditya/OpenBioLLM-Llama3-70B}} } ``` The accompanying paper is currently in progress and will be released soon. <div align="center"> <h2> 💌 Contact </h2> </div> We look forward to hearing you and collaborating on this exciting project! Contributors:** - [Ankit Pal (Aaditya Ura)](https://aadityaura.github.io/) [aadityaura at gmail dot com] - Saama AI Labs - Note: I am looking for a funded PhD opportunity, especially if it fits my Responsible Generative AI, Multimodal LLMs, Geometric Deep Learning, and Healthcare AI skillset. # References We thank the [Meta Team](meta-llama/Meta-Llama-3-70B-Instruct) for their amazing models! Result sources - [1] GPT-4 [Capabilities of GPT-4 on Medical Challenge Problems] (https://arxiv.org/abs/2303.13375) - [2] Med-PaLM-1 [Large Language Models Encode Clinical Knowledge](https://arxiv.org/abs/2212.13138) - [3] Med-PaLM-2 [Towards Expert-Level Medical Question Answering with Large Language Models](https://arxiv.org/abs/2305.09617) - [4] Gemini-1.0 [Gemini Goes to Med School](https://arxiv.org/abs/2402.07023)	{"language": ["en"], "license": "llama3", "tags": ["llama-3", "llama", "Mixtral", "instruct", "finetune", "chatml", "DPO", "RLHF", "gpt4", "distillation"], "base_model": "meta-llama/Meta-Llama-3-70B-Instruct", "widget": [{"example_title": "OpenBioLLM-70B", "messages": [{"role": "system", "content": "You are an expert and experienced from the healthcare and biomedical domain with extensive medical knowledge and practical experience."}, {"role": "user", "content": "How long does it take for newborn jaundice to go away?"}], "output": {"text": "Newborn jaundice, also known as neonatal jaundice, is a common condition in newborns where the yellowing of the skin and eyes occurs due to an elevated level of bilirubin in the blood. Bilirubin is a yellow pigment that forms when red blood cells break down. In most cases, newborn jaundice resolves on its own without any specific treatment.\nThe duration of newborn jaundice can vary depending on several factors such as the underlying cause, gestational age at birth, and individual variations in bilirubin metabolism. Here are some general guidelines:\n1. Physiological jaundice: This is the most common type of newborn jaundice and usually appears within 24-72 hours after birth. It tends to peak between the second and fifth day of life and gradually improves over the next week or two. By the time the baby is one week old, the jaundice should have mostly resolved. 2. Breast milk jaundice: This type of jaundice occurs in breastfed babies and may appear later than physiological jaundice, typically between the fifth and fourteenth day of life. It tends to persist for a longer duration but usually resolves within six weeks after birth. 3. Pathological jaundice: This type of jaundice is less common and occurs due to an underlying medical condition that affects bilirubin metabolism or liver function. The duration of pathological jaundice depends on the specific cause and may require treatment.\nIt's important for parents to monitor their newborn's jaundice closely and seek medical advice if the jaundice progresses rapidly, becomes severe, or is accompanied by other symptoms such as poor feeding, lethargy, or excessive sleepiness. In these cases, further evaluation and management may be necessary. Remember that each baby is unique, and the timing of jaundice resolution can vary. If you have concerns about your newborn's jaundice, it's always best to consult with a healthcare professional for personalized advice and guidance."}}], "model-index": [{"name": "OpenBioLLM-70B", "results": []}]}	LoneStriker/OpenBioLLM-Llama3-70B-GGUF	null	[ "gguf", "llama-3", "llama", "Mixtral", "instruct", "finetune", "chatml", "DPO", "RLHF", "gpt4", "distillation", "en", "arxiv:2305.18290", "arxiv:2303.13375", "arxiv:2212.13138", "arxiv:2305.09617", "arxiv:2402.07023", "base_model:meta-llama/Meta-Llama-3-70B-Instruct", "license:llama3", "region:us" ]	null	2024-04-26T20:56:57+00:00	[ "2305.18290", "2303.13375", "2212.13138", "2305.09617", "2402.07023" ]	[ "en" ]	TAGS #gguf #llama-3 #llama #Mixtral #instruct #finetune #chatml #DPO #RLHF #gpt4 #distillation #en #arxiv-2305.18290 #arxiv-2303.13375 #arxiv-2212.13138 #arxiv-2305.09617 #arxiv-2402.07023 #base_model-meta-llama/Meta-Llama-3-70B-Instruct #license-llama3 #region-us	![](URL <p>!image/png</p> <div align=) Advancing Open-source Large Language Models in Medical Domain ============================================================= Online Demo \| GitHub \| [![](URL alt=)](#) \| Discord !image/jpeg Introducing OpenBioLLM-70B: A State-of-the-Art Open Source Biomedical Large Language Model OpenBioLLM-70B is an advanced open source language model designed specifically for the biomedical domain. Developed by Saama AI Labs, this model leverages cutting-edge techniques to achieve state-of-the-art performance on a wide range of biomedical tasks. Biomedical Specialization: OpenBioLLM-70B is tailored for the unique language and knowledge requirements of the medical and life sciences fields. It was fine-tuned on a vast corpus of high-quality biomedical data, enabling it to understand and generate text with domain-specific accuracy and fluency. Superior Performance: With 70 billion parameters, OpenBioLLM-70B outperforms other open source biomedical language models of similar scale. It has also demonstrated better results compared to larger proprietary & open-source models like GPT-4, Gemini, Meditron-70B, Med-PaLM-1 & Med-PaLM-2 on biomedical benchmarks. Advanced Training Techniques: OpenBioLLM-70B builds upon the powerful foundations of the Meta-Llama-3-70B-Instruct and Meta-Llama-3-70B-Instruct models. It incorporates the DPO dataset and fine-tuning recipe along with a custom diverse medical instruction dataset. Key components of the training pipeline include: ![](URL </div> <ul> <li>Policy Optimization: Direct Preference Optimization: Your Language Model is Secretly a Reward Model (DPO)</li> <li>Fine-tuning dataset: Custom Medical Instruct dataset (We plan to release a sample training dataset in our upcoming paper; please stay updated)</li> </ul> <p>This combination of cutting-edge techniques enables OpenBioLLM-70B to align with key capabilities and preferences for biomedical applications.</p> <p>️ Release Details:</p> <ul> <li>Model Size: 70 billion parameters</li> <li>Quantization: Optimized quantized versions available Here</li> <li>Language(s) (NLP): en</li> <li>Developed By: Ankit Pal (Aaditya Ura) from Saama AI Labs</li> <li>License: Meta-Llama License</li> <li>Fine-tuned from models: Meta-Llama-3-70B-Instruct</li> <li>Resources for more information: <ul> <li>Paper: Coming soon</li> </ul> </li> </ul> <p>The model can be fine-tuned for more specialized tasks and datasets as needed.</p> <p>OpenBioLLM-70B represents an important step forward in democratizing advanced language AI for the biomedical community. By leveraging state-of-the-art architectures and training techniques from leading open source efforts like Llama-3, we have created a powerful tool to accelerate innovation and discovery in healthcare and the life sciences.</p> <p>We are excited to share OpenBioLLM-70B with researchers and developers around the world.</p> <h3>Use with transformers</h3> <p>Important: Please use the exact chat template provided by Llama-3 instruct version. Otherwise there will be a degradation in the performance. The model output can be verbose in rare cases. Please consider setting temperature = 0 to make this happen less.</p> <p>See the snippet below for usage with Transformers:</p> <h2>Training procedure</h2> <h3>Training hyperparameters</h3> <details> <summary>Click to see details</summary> <ul> <li>learning_rate: 0.0002</li> <li>lr_scheduler: cosine</li> <li>train_batch_size: 12</li> <li>eval_batch_size: 8</li> <li>GPU: H100 80GB SXM5</li> <li>num_devices: 8</li> <li>optimizer: adamw_bnb_8bit</li> <li>lr_scheduler_warmup_steps: 100</li> <li>num_epochs: 4</li> </ul> </details> <h3>Peft hyperparameters</h3> <details> <summary>Click to see details</summary> <ul> <li>adapter: qlora</li> <li>lora_r: 128</li> <li>lora_alpha: 256</li> <li>lora_dropout: 0.05</li> <li>lora_target_linear: true</li> </ul> <p>-lora_target_modules:</p> <ul> <li>q_proj</li> <li>v_proj</li> <li>k_proj</li> <li>o_proj</li> <li>gate_proj</li> <li>down_proj</li> <li>up_proj</li> </ul> </details> <h3>Training results</h3> <h3>Framework versions</h3> <ul> <li>Transformers 4.39.3</li> <li>Pytorch 2.1.2+cu121</li> <li>Datasets 2.18.0</li> <li>Tokenizers 0.15.1</li> <li>Axolotl</li> <li>Lm harness for evaluation</li> </ul> <h1>Benchmark Results</h1> <p>OpenBioLLM-70B demonstrates superior performance compared to larger models, such as GPT-4, Gemini, Meditron-70B, Med-PaLM-1 & Med-PaLM-2 across 9 diverse biomedical datasets, achieving state-of-the-art results with an average score of 86.06%, despite having a significantly smaller parameter count. The model's strong performance in domain-specific tasks, such as Clinical KG, Medical Genetics, and PubMedQA, highlights its ability to effectively capture and apply biomedical knowledge.</p> <p>The GPT-4, Med-PaLM-1, and Med-PaLM-2 results are taken from their official papers. Since Med-PaLM doesn't provide zero-shot accuracy, we are using 5-shot accuracy from their paper for comparison. All results presented are in the zero-shot setting, except for Med-PaLM-2 and Med-PaLM-1, which use 5-shot accuracy.</p> <p></p> <div align=) ![](URL </div> <h2>Detailed Medical Subjectwise accuracy</h2> <p>!image/png</p> <h1>Use Cases & Examples</h1> <p>Below results are from the quantized version of OpenBioLLM-70B</p> <h1>Summarize Clinical Notes</h1> <p>OpenBioLLM-70B can efficiently analyze and summarize complex clinical notes, EHR data, and discharge summaries, extracting key information and generating concise, structured summaries</p> <p>!image/png</p> <h1>Answer Medical Questions</h1> <p>OpenBioLLM-70B can provide answers to a wide range of medical questions.</p> <p>!image/png !image/png</p> <details> <summary>Click to see details</summary> <p>!image/png !image/png !image/png</p> </details> <h1>Clinical Entity Recognition</h1> <p>OpenBioLLM-70B can perform advanced clinical entity recognition by identifying and extracting key medical concepts, such as diseases, symptoms, medications, procedures, and anatomical structures, from unstructured clinical text. By leveraging its deep understanding of medical terminology and context, the model can accurately annotate and categorize clinical entities, enabling more efficient information retrieval, data analysis, and knowledge discovery from electronic health records, research articles, and other biomedical text sources. This capability can support various downstream applications, such as clinical decision support, pharmacovigilance, and medical research.</p> <p>!image/png !image/png !image/png</p> <h1>Biomarkers Extraction</h1> <p>!image/png</p> <h1>Classification</h1> <p>OpenBioLLM-70B can perform various biomedical classification tasks, such as disease prediction, sentiment analysis, medical document categorization</p> <p>!image/png</p> <h1>De-Identification</h1> <p>OpenBioLLM-70B can detect and remove personally identifiable information (PII) from medical records, ensuring patient privacy and compliance with data protection regulations like HIPAA.</p> <p>!image/png</p> <p>Advisory Notice!</p> <p>While OpenBioLLM-70B leverages high-quality data sources, its outputs may still contain inaccuracies, biases, or misalignments that could pose risks if relied upon for medical decision-making without further testing and refinement. The model's performance has not yet been rigorously evaluated in randomized controlled trials or real-world healthcare environments.</p> <p>Therefore, we strongly advise against using OpenBioLLM-70B for any direct patient care, clinical decision support, or other professional medical purposes at this time. Its use should be limited to research, development, and exploratory applications by qualified individuals who understand its limitations. OpenBioLLM-70B is intended solely as a research tool to assist healthcare professionals and should never be considered a replacement for the professional judgment and expertise of a qualified medical doctor.</p> <p>Appropriately adapting and validating OpenBioLLM-70B for specific medical use cases would require significant additional work, potentially including:</p> <ul> <li>Thorough testing and evaluation in relevant clinical scenarios</li> <li>Alignment with evidence-based guidelines and best practices</li> <li>Mitigation of potential biases and failure modes</li> <li>Integration with human oversight and interpretation</li> <li>Compliance with regulatory and ethical standards</li> </ul> <p>Always consult a qualified healthcare provider for personal medical needs.</p> <p>If you find OpenBioLLM-70B & 8B useful in your work, please cite the model as follows:</p> <p>The accompanying paper is currently in progress and will be released soon.</p> <div align=) Contact -------- We look forward to hearing you and collaborating on this exciting project! Contributors: * Ankit Pal (Aaditya Ura) [aadityaura at gmail dot com] * Saama AI Labs * Note: I am looking for a funded PhD opportunity, especially if it fits my Responsible Generative AI, Multimodal LLMs, Geometric Deep Learning, and Healthcare AI skillset. References ========== We thank the Meta Team for their amazing models! Result sources * [1] GPT-4 [Capabilities of GPT-4 on Medical Challenge Problems] (URL * [2] Med-PaLM-1 Large Language Models Encode Clinical Knowledge * [3] Med-PaLM-2 Towards Expert-Level Medical Question Answering with Large Language Models * [4] Gemini-1.0 Gemini Goes to Med School	[]	[ "TAGS\n#gguf #llama-3 #llama #Mixtral #instruct #finetune #chatml #DPO #RLHF #gpt4 #distillation #en #arxiv-2305.18290 #arxiv-2303.13375 #arxiv-2212.13138 #arxiv-2305.09617 #arxiv-2402.07023 #base_model-meta-llama/Meta-Llama-3-70B-Instruct #license-llama3 #region-us \n" ]

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