librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-07-27 18:27:08	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 533 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-27 18:22:57	card stringlengths 11 1.01M
1aurent/q-Taxi-v3	1aurent	2023-07-10T13:25:59Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T13:02:36Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="1aurent/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
Neko-Institute-of-Science/guanaco-unchained-33b-qlora	Neko-Institute-of-Science	2023-07-10T13:24:08Z	0	3	null	[ "dataset:CheshireAI/guanaco-unchained", "region:us" ]	null	2023-07-10T00:10:04Z	--- datasets: - CheshireAI/guanaco-unchained --- Let's see how this goes. Training in 8 bit and at full context. Is 8bit even a qlora? ``` python qlora.py \ --model_name_or_path /UI/text-generation-webui/models/llama-30b \ --output_dir ./output/guanaco-33b \ --logging_steps 1 \ --save_strategy steps \ --data_seed 42 \ --save_steps 69 \ --save_total_limit 999 \ --per_device_eval_batch_size 1 \ --dataloader_num_workers 3 \ --group_by_length \ --logging_strategy steps \ --remove_unused_columns False \ --do_train \ --do_eval false \ --do_mmlu_eval false \ --lora_r 64 \ --lora_alpha 16 \ --lora_modules all \ --bf16 \ --bits 8 \ --warmup_ratio 0.03 \ --lr_scheduler_type constant \ --gradient_checkpointing \ --gradient_accumulation_steps 32 \ --dataset oasst1 \ --source_max_len 2048 \ --target_max_len 2048 \ --per_device_train_batch_size 1 \ --num_train_epochs 3 \ --learning_rate 0.0001 \ --adam_beta2 0.999 \ --max_grad_norm 0.3 \ --lora_dropout 0.05 \ --weight_decay 0.0 \ --seed 0 ```
TheBloke/Chronoboros-33B-GGML	TheBloke	2023-07-10T13:16:31Z	0	11	null	[ "license:other", "region:us" ]	null	2023-07-10T08:29:30Z	--- inference: false license: other --- <!-- header start --> <div style="width: 100%;"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p><a href="https://discord.gg/theblokeai">Chat & support: my new Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <!-- header end --> # Henk717's Chronoboros 33B GGML These files are GGML format model files for [Henk717's Chronoboros 33B](https://huggingface.co/Henk717/chronoboros-33B). GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as: * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a powerful GGML web UI with full GPU acceleration out of the box. Especially good for story telling. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with GPU acceleration via the c_transformers backend. * [LM Studio](https://lmstudio.ai/), a fully featured local GUI. Supports full GPU accel on macOS. Also supports Windows, without GPU accel. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most popular web UI. Requires extra steps to enable GPU accel via llama.cpp backend. * [ctransformers](https://github.com/marella/ctransformers), a Python library with LangChain support and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with OpenAI-compatible API server. ## Repositories available * [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/Chronoboros-33B-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/Chronoboros-33B-GGML) * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Henk717/chronoboros-33B) ## Prompt template: Alpaca ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ``` <!-- compatibility_ggml start --> ## Compatibility ### Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0` These are guaranteed to be compatible with any UIs, tools and libraries released since late May. They may be phased out soon, as they are largely superseded by the new k-quant methods. ### New k-quant methods: `q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K` These new quantisation methods are compatible with llama.cpp as of June 6th, commit `2d43387`. They are now also compatible with recent releases of text-generation-webui, KoboldCpp, llama-cpp-python, ctransformers, rustformers and most others. For compatibility with other tools and libraries, please check their documentation. ## Explanation of the new k-quant methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw * GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type. Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_ggml end --> ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| chronoboros-33b.ggmlv3.q2_K.bin \| q2_K \| 2 \| 13.71 GB\| 16.21 GB \| New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. \| \| chronoboros-33b.ggmlv3.q3_K_S.bin \| q3_K_S \| 3 \| 14.06 GB\| 16.56 GB \| New k-quant method. Uses GGML_TYPE_Q3_K for all tensors \| \| chronoboros-33b.ggmlv3.q3_K_M.bin \| q3_K_M \| 3 \| 15.72 GB\| 18.22 GB \| New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K \| \| chronoboros-33b.ggmlv3.q3_K_L.bin \| q3_K_L \| 3 \| 17.28 GB\| 19.78 GB \| New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K \| \| chronoboros-33b.ggmlv3.q4_K_S.bin \| q4_K_S \| 4 \| 18.36 GB\| 20.86 GB \| New k-quant method. Uses GGML_TYPE_Q4_K for all tensors \| \| chronoboros-33b.ggmlv3.q4_K_M.bin \| q4_K_M \| 4 \| 19.62 GB\| 22.12 GB \| New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K \| \| chronoboros-33b.ggmlv3.q4_0.bin \| q4_0 \| 4 \| 18.30 GB\| 20.80 GB \| Original quant method, 4-bit. \| \| chronoboros-33b.ggmlv3.q4_1.bin \| q4_1 \| 4 \| 20.33 GB\| 22.83 GB \| Original quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. \| \| chronoboros-33b.ggmlv3.q5_K_S.bin \| q5_K_S \| 5 \| 22.40 GB\| 24.90 GB \| New k-quant method. Uses GGML_TYPE_Q5_K for all tensors \| \| chronoboros-33b.ggmlv3.q5_K_M.bin \| q5_K_M \| 5 \| 23.05 GB\| 25.55 GB \| New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K \| \| chronoboros-33b.ggmlv3.q5_0.bin \| q5_0 \| 5 \| 22.37 GB\| 24.87 GB \| Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. \| \| chronoboros-33b.ggmlv3.q5_1.bin \| q5_1 \| 5 \| 24.40 GB\| 26.90 GB \| Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference. \| \| chronoboros-33b.ggmlv3.q6_K.bin \| q6_K \| 6 \| 26.69 GB\| 29.19 GB \| New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors \| \| chronoboros-33b.ggmlv3.q8_0.bin \| q8_0 \| 8 \| 34.56 GB\| 37.06 GB \| Original quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. ## How to run in `llama.cpp` I use the following command line; adjust for your tastes and needs: ``` ./main -t 10 -ngl 32 -m chronoboros-33b.ggmlv3.q4_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: Write a story about llamas\n### Response:" ``` Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`. Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md). <!-- footer start --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute. Thanks to the [chirper.ai](https://chirper.ai) team! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Luke from CarbonQuill, Aemon Algiz. Patreon special mentions: RoA, Lone Striker, Gabriel Puliatti, Derek Yates, Randy H, Jonathan Leane, Eugene Pentland, Karl Bernard, Viktor Bowallius, senxiiz, Daniel P. Andersen, Pierre Kircher, Deep Realms, Cory Kujawski, Oscar Rangel, Fen Risland, Ajan Kanaga, LangChain4j, webtim, Nikolai Manek, Trenton Dambrowitz, Raven Klaugh, Kalila, Khalefa Al-Ahmad, Chris McCloskey, Luke @flexchar, Ai Maven, Dave, Asp the Wyvern, Sean Connelly, Imad Khwaja, Space Cruiser, Rainer Wilmers, subjectnull, Alps Aficionado, Willian Hasse, Fred von Graf, Artur Olbinski, Johann-Peter Hartmann, WelcomeToTheClub, Willem Michiel, Michael Levine, Iucharbius , Spiking Neurons AB, K, biorpg, John Villwock, Pyrater, Greatston Gnanesh, Mano Prime, Junyu Yang, Stephen Murray, John Detwiler, Luke Pendergrass, terasurfer , Pieter, zynix , Edmond Seymore, theTransient, Nathan LeClaire, vamX, Kevin Schuppel, Preetika Verma, ya boyyy, Alex , SuperWojo, Ghost , Joseph William Delisle, Matthew Berman, Talal Aujan, chris gileta, Illia Dulskyi. Thank you to all my generous patrons and donaters! <!-- footer end --> # Original model card: Henk717's Chronoboros 33B This model was the result of a 50/50 average weight merge between Airoboros-33B-1.4 and Chronos-33B. License is inhereted from all merged models, which includes the LLaMA license requiring you to own a license to use the LLaMA models. If you have such a license grant from Facebook you can request access to this model.
Knudo/distilbert-base-uncased-finetuned-cola	Knudo	2023-07-10T13:13:46Z	61	0	transformers	[ "transformers", "tf", "tensorboard", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-10T13:09:20Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Knudo/distilbert-base-uncased-finetuned-cola results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # Knudo/distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1842 - Validation Loss: 0.5764 - Train Matthews Correlation: 0.5185 - Epoch: 2 ## 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: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1602, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Matthews Correlation \| Epoch \| \|:----------:\|:---------------:\|:--------------------------:\|:-----:\| \| 0.5106 \| 0.4629 \| 0.4797 \| 0 \| \| 0.3111 \| 0.4999 \| 0.4957 \| 1 \| \| 0.1842 \| 0.5764 \| 0.5185 \| 2 \| ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3
jordyvl/vit-tiny_tobacco3482_kd_MSE_test_pretrain_student	jordyvl	2023-07-10T13:01:47Z	163	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-10T12:59:18Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: vit-tiny_tobacco3482_kd_MSE_test_pretrain_student results: [] --- <!-- 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. --> # vit-tiny_tobacco3482_kd_MSE_test_pretrain_student This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None 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: 32 - eval_batch_size: 32 - 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: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 25 \| 0.6243 \| 0.595 \| 0.6456 \| 1.9017 \| 0.595 \| 0.5113 \| 0.3512 \| 0.2202 \| ### Framework versions - Transformers 4.28.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.12.0 - Tokenizers 0.12.1
datenmassiv/falcon-7b-instruct	datenmassiv	2023-07-10T13:00:34Z	13	0	transformers	[ "transformers", "pytorch", "coreml", "RefinedWebModel", "text-generation", "custom_code", "en", "dataset:tiiuae/falcon-refinedweb", "arxiv:2205.14135", "arxiv:1911.02150", "arxiv:2005.14165", "arxiv:2104.09864", "arxiv:2306.01116", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-10T13:00:33Z	--- datasets: - tiiuae/falcon-refinedweb language: - en inference: true widget: - text: Hey Falcon! Any recommendations for my holidays in Abu Dhabi? example_title: Abu Dhabi Trip - text: What's the Everett interpretation of quantum mechanics? example_title: 'Q/A: Quantum & Answers' - text: >- Give me a list of the top 10 dive sites you would recommend around the world. example_title: Diving Top 10 - text: Can you tell me more about deep-water soloing? example_title: Extreme sports - text: >- Can you write a short tweet about the Apache 2.0 release of our latest AI model, Falcon LLM? example_title: Twitter Helper - text: What are the responsabilities of a Chief Llama Officer? example_title: Trendy Jobs license: apache-2.0 duplicated_from: tiiuae/falcon-7b-instruct --- # ✨ Falcon-7B-Instruct Falcon-7B-Instruct is a 7B parameters causal decoder-only model built by [TII](https://www.tii.ae) based on [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b) and finetuned on a mixture of chat/instruct datasets. It is made available under the Apache 2.0 license. Paper coming soon 😊. 🤗 To get started with Falcon (inference, finetuning, quantization, etc.), we recommend reading [this great blogpost fron HF](https://huggingface.co/blog/falcon)! ## Why use Falcon-7B-Instruct? * You are looking for a ready-to-use chat/instruct model based on [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b). * Falcon-7B is a strong base model, outperforming comparable open-source models (e.g., [MPT-7B](https://huggingface.co/mosaicml/mpt-7b), [StableLM](https://github.com/Stability-AI/StableLM), [RedPajama](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-7B-v0.1) etc.), thanks to being trained on 1,500B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) enhanced with curated corpora. See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). * It features an architecture optimized for inference, with FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)) and multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)). 💬 This is an instruct model, which may not be ideal for further finetuning. If you are interested in building your own instruct/chat model, we recommend starting from [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b). 🔥 Looking for an even more powerful model? [Falcon-40B-Instruct](https://huggingface.co/tiiuae/falcon-40b-instruct) is Falcon-7B-Instruct's big brother! ```python from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model = "tiiuae/falcon-7b-instruct" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, tokenizer=tokenizer, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", ) sequences = pipeline( "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:", max_length=200, do_sample=True, top_k=10, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ``` 💥 Falcon LLMs require PyTorch 2.0 for use with `transformers`! For fast inference with Falcon, check-out [Text Generation Inference](https://github.com/huggingface/text-generation-inference)! Read more in this [blogpost]((https://huggingface.co/blog/falcon). You will need at least 16GB of memory to swiftly run inference with Falcon-7B-Instruct. # Model Card for Falcon-7B-Instruct ## Model Details ### Model Description - Developed by: [https://www.tii.ae](https://www.tii.ae); - Model type: Causal decoder-only; - Language(s) (NLP): English and French; - License: Apache 2.0; - Finetuned from model: [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b). ### Model Source - Paper: coming soon. ## Uses ### Direct Use Falcon-7B-Instruct has been finetuned on a mixture of instruct and chat datasets. ### Out-of-Scope Use Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful. ## Bias, Risks, and Limitations Falcon-7B-Instruct is mostly trained on English data, and will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online. ### Recommendations We recommend users of Falcon-7B-Instruct to develop guardrails and to take appropriate precautions for any production use. ## How to Get Started with the Model ```python from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model = "tiiuae/falcon-7b-instruct" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, tokenizer=tokenizer, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", ) sequences = pipeline( "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:", max_length=200, do_sample=True, top_k=10, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ``` ## Training Details ### Training Data Falcon-7B-Instruct was finetuned on a 250M tokens mixture of instruct/chat datasets. \| Data source \| Fraction \| Tokens \| Description \| \|--------------------\|--------------\|------------\|-----------------------------------\| \| [Bai ze](https://github.com/project-baize/baize-chatbot) \| 65% \| 164M \| chat \| \| [GPT4All](https://github.com/nomic-ai/gpt4all) \| 25% \| 62M \| instruct \| \| [GPTeacher](https://github.com/teknium1/GPTeacher) \| 5% \| 11M \| instruct \| \| [RefinedWeb-English](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) \| 5% \| 13M \| massive web crawl \| The data was tokenized with the Falcon-[7B](https://huggingface.co/tiiuae/falcon-7b)/[40B](https://huggingface.co/tiiuae/falcon-40b) tokenizer. ## Evaluation Paper coming soon. See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) for early results. Note that this model variant is not optimized for NLP benchmarks. ## Technical Specifications For more information about pretraining, see [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b). ### Model Architecture and Objective Falcon-7B is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token). The architecture is broadly adapted from the GPT-3 paper ([Brown et al., 2020](https://arxiv.org/abs/2005.14165)), with the following differences: * Positionnal embeddings: rotary ([Su et al., 2021](https://arxiv.org/abs/2104.09864)); * Attention: multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)) and FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)); * Decoder-block: parallel attention/MLP with a single layer norm. \| Hyperparameter \| Value \| Comment \| \|--------------------\|-----------\|----------------------------------------\| \| Layers \| 32 \| \| \| `d_model` \| 4544 \| Increased to compensate for multiquery \| \| `head_dim` \| 64 \| Reduced to optimise for FlashAttention \| \| Vocabulary \| 65024 \| \| \| Sequence length \| 2048 \| \| ### Compute Infrastructure #### Hardware Falcon-7B-Instruct was trained on AWS SageMaker, on 32 A100 40GB GPUs in P4d instances. #### Software Falcon-7B-Instruct was trained a custom distributed training codebase, Gigatron. It uses a 3D parallelism approach combined with ZeRO and high-performance Triton kernels (FlashAttention, etc.) ## Citation Paper coming soon 😊. In the meanwhile, you can use the following information to cite: ``` @article{falcon40b, title={{Falcon-40B}: an open large language model with state-of-the-art performance}, author={Almazrouei, Ebtesam and Alobeidli, Hamza and Alshamsi, Abdulaziz and Cappelli, Alessandro and Cojocaru, Ruxandra and Debbah, Merouane and Goffinet, Etienne and Heslow, Daniel and Launay, Julien and Malartic, Quentin and Noune, Badreddine and Pannier, Baptiste and Penedo, Guilherme}, year={2023} } ``` To learn more about the pretraining dataset, see the 📓 [RefinedWeb paper](https://arxiv.org/abs/2306.01116). ``` @article{refinedweb, title={The {R}efined{W}eb dataset for {F}alcon {LLM}: outperforming curated corpora with web data, and web data only}, author={Guilherme Penedo and Quentin Malartic and Daniel Hesslow and Ruxandra Cojocaru and Alessandro Cappelli and Hamza Alobeidli and Baptiste Pannier and Ebtesam Almazrouei and Julien Launay}, journal={arXiv preprint arXiv:2306.01116}, eprint={2306.01116}, eprinttype = {arXiv}, url={https://arxiv.org/abs/2306.01116}, year={2023} } ``` ## License Falcon-7B-Instruct is made available under the Apache 2.0 license. ## Contact [email protected]
ccattomio/Reinforce-CartPole-v1	ccattomio	2023-07-10T12:59:48Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T12:59:37Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
1aurent/q-FrozenLake-v1-4x4-noSlippery	1aurent	2023-07-10T12:58:37Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T12:58:33Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="1aurent/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
bonzo1971/sesgo_genero_model	bonzo1971	2023-07-10T12:46:57Z	105	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-09T14:10:18Z	--- tags: - generated_from_trainer model-index: - name: sesgo_genero_model results: [] --- <!-- 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. --> # sesgo_genero_model This model is a fine-tuned version of [pysentimiento/robertuito-base-uncased](https://huggingface.co/pysentimiento/robertuito-base-uncased) on the None 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: 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 ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
GraydientPlatformAPI/model_710du	GraydientPlatformAPI	2023-07-10T12:30:48Z	29	0	diffusers	[ "diffusers", "text-to-image", "license:openrail", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-10T12:10:24Z	--- license: openrail library_name: diffusers pipeline_tag: text-to-image ---
Nianhua123/ppo-LunarLander-v2	Nianhua123	2023-07-10T12:24:32Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T12:24:15Z	--- 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: 269.69 +/- 15.15 name: mean_reward verified: false --- # 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 ... ```
PraveenJesu/openai-whisper-medium-peft-lora-v2.2.4	PraveenJesu	2023-07-10T12:24:27Z	1	0	peft	[ "peft", "region:us" ]	null	2023-07-10T12:24:25Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0
KaraAgroAI/CADI-AI	KaraAgroAI	2023-07-10T12:23:56Z	21	3	yolo	[ "yolo", "object detection", "vision", "object-detection", "en", "dataset:KaraAgroAI/CADI-AI", "license:agpl-3.0", "region:us" ]	object-detection	2023-05-17T13:54:39Z	--- license: agpl-3.0 datasets: - KaraAgroAI/CADI-AI language: - en library_name: yolo tags: - object detection - vision - yolo pipeline_tag: object-detection metrics: - mape --- ## Cashew Disease Identification with AI (CADI-AI) Model ### Model Description Object detection model trained using [YOLO v5x](https://github.com/ultralytics/yolov5/releases), a SOTA object detection algorithm. The model was pre-trained on the Cashew Disease Identification with AI (CADI-AI) train set (3788 images) at a resolution of 640x640 pixels. The CADI-AI dataset is available via [Kaggle](https://www.kaggle.com/datasets/karaagroaiprojects/cadi-ai) and [HuggingFace](https://huggingface.co/datasets/KaraAgroAI/CADI-AI). ## Intended uses You can use the raw model for object detection on cashew images. The model was initially developed to inform users whether cashew trees suffer from: - pest infection, i.e. damage to crops by insects or pests - disease, i.e. attacks on crops by microorganisms - abiotic stress caused by non-living factors (e.g. environmental factors like weather or soil conditions or the lack of mineral nutrients to the crop). KaraAgro AI developed the model for the initiatives [Market-Oriented Value Chains for Jobs & Growth in the ECOWAS Region (MOVE)](https://www.giz.de/en/worldwide/108524.html) and [FAIR Forward - Artificial Intelligence for All](https://www.bmz-digital.global/en/overview-of-initiatives/fair-forward/). Both initiatives are implemented by the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) on behalf of the German Federal Ministry for Economic Cooperation and Development (BMZ). ### How to use - Load model and perform prediction: ```bash pip install -U ultralytics ``` ```python import torch # load model model = torch.hub.load('ultralytics/yolov5', 'custom', path='CADI-AI/yolov5_0.65map_exp7_best.pt', force_reload=True) # Images img = ['/path/to/your/image.jpg']# batch of images # set model parameters # set Non-Maximum-Suppression(NMS) threshold to define # minimum confidence score that a bounding box must have in order to be kept. model.conf = 0.20 # NMS confidence threshold # perform inference results = model(img, size=640) # Results results.print() results.xyxy[0] # img1 predictions (tensor) results.pandas().xyxy[0] # img1 predictions (pandas) # parse results predictions = results.pred[0] boxes = predictions[:, :4] # x1, y1, x2, y2 scores = predictions[:, 4] categories = predictions[:, 5] # show detection bounding boxes on image results.show() # save results into "results/" folder results.save(save_dir='results/') ``` - Finetune the model on your custom dataset: ```bash yolov5 train --data data.yaml --img 640 --batch 16 --weights KaraAgroAI/CADI-AI --epochs 10 ``` ### Model performance \| Class \| Precision \| Recall \| mAP@50 \| mAP@50-95 \| \| --- \| --- \| --- \| --- \| --- \| \| all \| 0.663 \| 0.632 \| 0.648 \| 0.291 \| \| insect \| 0.794 \| 0.811 \| 0.815 \| 0.39 \| \| abiotic \| 0.682 \| 0.514 \| 0.542 \| 0.237 \| \| disease \| 0.594 \| 0.571 \| 0.588 \| 0.248 \| ### Limitations of the Model The model has a few limitations that affect its performance in distinguishing between the disease class and the abiotic class. The primary challenge lies in the similarity between these two classes within a typical farm setting. The model may encounter difficulties in accurately differentiating between them due to their overlapping characteristics. This limitation is an inherent challenge in the dataset and can impact the model's accuracy when classifying these classes. However, it is worth noting that the model exhibits strong performance when it comes to the insect class. This is attributed to the distinct characteristics of insect class, which make them easier to identify and classify accurately. ### Demo [CADI-AI Spaces demonstration](https://huggingface.co/spaces/KaraAgroAI/CADI-AI) ### Project Repo If you want to know how the model and dataset has been used further for the GIZ-funded activity, please have a look at: - The [GitHub repository](https://github.com/karaagro/cadi-ai) for the CADI AI desktop application ### Example prediction <div align="center"> <img width="640" alt="KaraAgroAI/CADI-AI" src="https://huggingface.co/KaraAgroAI/CADI-AI/resolve/main/sample.jpg"> </div>
Mavila/First_DRL	Mavila	2023-07-10T12:06:41Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T12:06:19Z	--- 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: 261.12 +/- 13.54 name: mean_reward verified: false --- # 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 ... ```
jordyvl/dit-small_rvl_cdip_100_examples_per_class_kd_MSE_lr_fix	jordyvl	2023-07-10T12:04:08Z	161	0	transformers	[ "transformers", "pytorch", "beit", "image-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-10T11:10:29Z	--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: dit-small_rvl_cdip_100_examples_per_class_kd_MSE_lr_fix results: [] --- <!-- 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. --> # dit-small_rvl_cdip_100_examples_per_class_kd_MSE_lr_fix This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8796 - Accuracy: 0.26 - Brier Loss: 0.8768 - Nll: 6.0962 - F1 Micro: 0.26 - F1 Macro: 0.2480 - Ece: 0.2002 - Aurc: 0.5815 ## 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: 128 - eval_batch_size: 128 - 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: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:-------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 1.5365 \| 0.065 \| 0.9398 \| 10.2864 \| 0.065 \| 0.0116 \| 0.1183 \| 0.9536 \| \| No log \| 2.0 \| 14 \| 1.5332 \| 0.06 \| 0.9374 \| 9.8468 \| 0.06 \| 0.0269 \| 0.1067 \| 0.9096 \| \| No log \| 3.0 \| 21 \| 1.5119 \| 0.085 \| 0.9352 \| 9.1495 \| 0.085 \| 0.0355 \| 0.1135 \| 0.8759 \| \| No log \| 4.0 \| 28 \| 1.5040 \| 0.0825 \| 0.9333 \| 8.6549 \| 0.0825 \| 0.0439 \| 0.1181 \| 0.8618 \| \| No log \| 5.0 \| 35 \| 1.5021 \| 0.1 \| 0.9301 \| 8.9643 \| 0.1000 \| 0.0558 \| 0.1318 \| 0.8030 \| \| No log \| 6.0 \| 42 \| 1.4885 \| 0.1 \| 0.9276 \| 7.8684 \| 0.1000 \| 0.0505 \| 0.1205 \| 0.8190 \| \| No log \| 7.0 \| 49 \| 1.4882 \| 0.0975 \| 0.9254 \| 9.4095 \| 0.0975 \| 0.0584 \| 0.1220 \| 0.7847 \| \| No log \| 8.0 \| 56 \| 1.4909 \| 0.1275 \| 0.9227 \| 9.4274 \| 0.1275 \| 0.0827 \| 0.1335 \| 0.7445 \| \| No log \| 9.0 \| 63 \| 1.4837 \| 0.115 \| 0.9217 \| 10.2918 \| 0.115 \| 0.0546 \| 0.1366 \| 0.7932 \| \| No log \| 10.0 \| 70 \| 1.4857 \| 0.1125 \| 0.9186 \| 9.5039 \| 0.1125 \| 0.0510 \| 0.1277 \| 0.7749 \| \| No log \| 11.0 \| 77 \| 1.4804 \| 0.1125 \| 0.9183 \| 8.5178 \| 0.1125 \| 0.0515 \| 0.1315 \| 0.7831 \| \| No log \| 12.0 \| 84 \| 1.4701 \| 0.11 \| 0.9177 \| 8.2398 \| 0.11 \| 0.0655 \| 0.1310 \| 0.7754 \| \| No log \| 13.0 \| 91 \| 1.4721 \| 0.16 \| 0.9160 \| 7.2379 \| 0.16 \| 0.1155 \| 0.1462 \| 0.7370 \| \| No log \| 14.0 \| 98 \| 1.4717 \| 0.11 \| 0.9159 \| 8.1355 \| 0.11 \| 0.0633 \| 0.1221 \| 0.7579 \| \| No log \| 15.0 \| 105 \| 1.4739 \| 0.1325 \| 0.9138 \| 7.4037 \| 0.1325 \| 0.0790 \| 0.1419 \| 0.7358 \| \| No log \| 16.0 \| 112 \| 1.4657 \| 0.1425 \| 0.9135 \| 7.8063 \| 0.1425 \| 0.0821 \| 0.1285 \| 0.7269 \| \| No log \| 17.0 \| 119 \| 1.4632 \| 0.1375 \| 0.9112 \| 7.8852 \| 0.1375 \| 0.0948 \| 0.1389 \| 0.7342 \| \| No log \| 18.0 \| 126 \| 1.4769 \| 0.15 \| 0.9081 \| 8.5375 \| 0.15 \| 0.0894 \| 0.1399 \| 0.7113 \| \| No log \| 19.0 \| 133 \| 1.4547 \| 0.1775 \| 0.9045 \| 6.4114 \| 0.1775 \| 0.1174 \| 0.1507 \| 0.7007 \| \| No log \| 20.0 \| 140 \| 1.4470 \| 0.1725 \| 0.9031 \| 8.1696 \| 0.1725 \| 0.1246 \| 0.1464 \| 0.7079 \| \| No log \| 21.0 \| 147 \| 1.4615 \| 0.19 \| 0.9021 \| 6.0696 \| 0.19 \| 0.1390 \| 0.1646 \| 0.7023 \| \| No log \| 22.0 \| 154 \| 1.4588 \| 0.2 \| 0.8996 \| 6.0038 \| 0.2000 \| 0.1384 \| 0.1628 \| 0.6821 \| \| No log \| 23.0 \| 161 \| 1.4646 \| 0.1525 \| 0.8988 \| 7.0678 \| 0.1525 \| 0.1075 \| 0.1458 \| 0.7000 \| \| No log \| 24.0 \| 168 \| 1.4491 \| 0.2125 \| 0.8933 \| 5.9276 \| 0.2125 \| 0.1503 \| 0.1533 \| 0.6457 \| \| No log \| 25.0 \| 175 \| 1.4526 \| 0.205 \| 0.8916 \| 7.6108 \| 0.205 \| 0.1479 \| 0.1603 \| 0.6676 \| \| No log \| 26.0 \| 182 \| 1.4510 \| 0.17 \| 0.8910 \| 5.6337 \| 0.17 \| 0.1333 \| 0.1396 \| 0.6868 \| \| No log \| 27.0 \| 189 \| 1.4567 \| 0.19 \| 0.8850 \| 5.2038 \| 0.19 \| 0.1380 \| 0.1637 \| 0.6547 \| \| No log \| 28.0 \| 196 \| 1.4570 \| 0.2225 \| 0.8846 \| 6.5368 \| 0.2225 \| 0.1840 \| 0.1701 \| 0.6554 \| \| No log \| 29.0 \| 203 \| 1.4701 \| 0.2075 \| 0.8820 \| 5.0057 \| 0.2075 \| 0.1663 \| 0.1719 \| 0.6598 \| \| No log \| 30.0 \| 210 \| 1.4693 \| 0.2225 \| 0.8755 \| 7.4456 \| 0.2225 \| 0.1729 \| 0.1626 \| 0.6355 \| \| No log \| 31.0 \| 217 \| 1.4670 \| 0.23 \| 0.8787 \| 5.8938 \| 0.23 \| 0.1904 \| 0.1717 \| 0.6424 \| \| No log \| 32.0 \| 224 \| 1.4540 \| 0.2275 \| 0.8756 \| 6.6513 \| 0.2275 \| 0.1673 \| 0.1676 \| 0.6306 \| \| No log \| 33.0 \| 231 \| 1.4641 \| 0.2275 \| 0.8649 \| 5.5689 \| 0.2275 \| 0.1751 \| 0.1746 \| 0.6138 \| \| No log \| 34.0 \| 238 \| 1.4710 \| 0.2425 \| 0.8640 \| 7.0556 \| 0.2425 \| 0.1957 \| 0.1809 \| 0.6048 \| \| No log \| 35.0 \| 245 \| 1.4685 \| 0.23 \| 0.8632 \| 5.5735 \| 0.23 \| 0.1940 \| 0.1609 \| 0.6188 \| \| No log \| 36.0 \| 252 \| 1.4665 \| 0.2375 \| 0.8592 \| 5.8835 \| 0.2375 \| 0.1952 \| 0.1727 \| 0.6050 \| \| No log \| 37.0 \| 259 \| 1.4668 \| 0.235 \| 0.8540 \| 5.3502 \| 0.235 \| 0.1966 \| 0.1746 \| 0.6056 \| \| No log \| 38.0 \| 266 \| 1.4855 \| 0.27 \| 0.8510 \| 5.3781 \| 0.27 \| 0.2124 \| 0.1692 \| 0.5825 \| \| No log \| 39.0 \| 273 \| 1.5279 \| 0.265 \| 0.8562 \| 6.2426 \| 0.265 \| 0.2126 \| 0.1772 \| 0.5831 \| \| No log \| 40.0 \| 280 \| 1.5433 \| 0.2425 \| 0.8551 \| 5.9574 \| 0.2425 \| 0.1867 \| 0.1499 \| 0.5874 \| \| No log \| 41.0 \| 287 \| 1.5955 \| 0.2525 \| 0.8597 \| 6.1628 \| 0.2525 \| 0.2024 \| 0.1479 \| 0.5891 \| \| No log \| 42.0 \| 294 \| 1.5528 \| 0.2475 \| 0.8541 \| 6.3624 \| 0.2475 \| 0.1908 \| 0.1566 \| 0.5735 \| \| No log \| 43.0 \| 301 \| 1.5858 \| 0.2675 \| 0.8504 \| 6.1261 \| 0.2675 \| 0.2174 \| 0.1706 \| 0.5674 \| \| No log \| 44.0 \| 308 \| 1.6013 \| 0.2725 \| 0.8496 \| 5.8409 \| 0.2725 \| 0.2463 \| 0.1846 \| 0.5807 \| \| No log \| 45.0 \| 315 \| 1.5632 \| 0.2625 \| 0.8472 \| 5.9669 \| 0.2625 \| 0.2307 \| 0.1689 \| 0.5689 \| \| No log \| 46.0 \| 322 \| 1.6520 \| 0.2675 \| 0.8509 \| 5.8544 \| 0.2675 \| 0.2325 \| 0.1779 \| 0.5622 \| \| No log \| 47.0 \| 329 \| 1.6135 \| 0.2625 \| 0.8476 \| 5.5208 \| 0.2625 \| 0.2504 \| 0.1565 \| 0.5759 \| \| No log \| 48.0 \| 336 \| 1.6565 \| 0.275 \| 0.8466 \| 5.9254 \| 0.275 \| 0.2527 \| 0.2026 \| 0.5616 \| \| No log \| 49.0 \| 343 \| 1.6807 \| 0.2625 \| 0.8531 \| 6.1297 \| 0.2625 \| 0.2259 \| 0.1813 \| 0.5664 \| \| No log \| 50.0 \| 350 \| 1.7266 \| 0.255 \| 0.8560 \| 6.0828 \| 0.255 \| 0.2315 \| 0.1817 \| 0.5735 \| \| No log \| 51.0 \| 357 \| 1.7038 \| 0.2525 \| 0.8579 \| 5.6442 \| 0.2525 \| 0.2405 \| 0.1861 \| 0.5828 \| \| No log \| 52.0 \| 364 \| 1.7954 \| 0.255 \| 0.8583 \| 5.7016 \| 0.255 \| 0.2227 \| 0.1722 \| 0.5725 \| \| No log \| 53.0 \| 371 \| 1.7567 \| 0.275 \| 0.8557 \| 6.1586 \| 0.275 \| 0.2523 \| 0.1577 \| 0.5619 \| \| No log \| 54.0 \| 378 \| 1.7589 \| 0.2525 \| 0.8565 \| 5.3969 \| 0.2525 \| 0.2325 \| 0.1840 \| 0.5661 \| \| No log \| 55.0 \| 385 \| 1.7778 \| 0.265 \| 0.8569 \| 5.8559 \| 0.265 \| 0.2447 \| 0.1835 \| 0.5640 \| \| No log \| 56.0 \| 392 \| 1.8044 \| 0.275 \| 0.8592 \| 5.9942 \| 0.275 \| 0.2517 \| 0.1783 \| 0.5627 \| \| No log \| 57.0 \| 399 \| 1.8327 \| 0.2625 \| 0.8628 \| 6.0224 \| 0.2625 \| 0.2333 \| 0.1801 \| 0.5560 \| \| No log \| 58.0 \| 406 \| 1.8184 \| 0.25 \| 0.8609 \| 6.0769 \| 0.25 \| 0.2333 \| 0.1941 \| 0.5718 \| \| No log \| 59.0 \| 413 \| 1.8318 \| 0.2575 \| 0.8639 \| 5.9454 \| 0.2575 \| 0.2364 \| 0.1965 \| 0.5743 \| \| No log \| 60.0 \| 420 \| 1.8081 \| 0.2525 \| 0.8641 \| 6.0119 \| 0.2525 \| 0.2380 \| 0.1818 \| 0.5755 \| \| No log \| 61.0 \| 427 \| 1.8405 \| 0.2625 \| 0.8775 \| 6.2129 \| 0.2625 \| 0.2474 \| 0.1767 \| 0.5908 \| \| No log \| 62.0 \| 434 \| 1.9012 \| 0.2625 \| 0.8728 \| 6.1015 \| 0.2625 \| 0.2373 \| 0.1881 \| 0.5716 \| \| No log \| 63.0 \| 441 \| 1.8500 \| 0.26 \| 0.8728 \| 6.3885 \| 0.26 \| 0.2414 \| 0.1933 \| 0.5809 \| \| No log \| 64.0 \| 448 \| 1.8771 \| 0.2675 \| 0.8733 \| 6.2730 \| 0.2675 \| 0.2553 \| 0.2035 \| 0.5800 \| \| No log \| 65.0 \| 455 \| 1.8744 \| 0.2575 \| 0.8677 \| 5.9805 \| 0.2575 \| 0.2392 \| 0.1918 \| 0.5663 \| \| No log \| 66.0 \| 462 \| 1.8366 \| 0.255 \| 0.8694 \| 6.0073 \| 0.255 \| 0.2403 \| 0.2048 \| 0.5807 \| \| No log \| 67.0 \| 469 \| 1.8758 \| 0.2575 \| 0.8743 \| 6.1015 \| 0.2575 \| 0.2381 \| 0.2071 \| 0.5825 \| \| No log \| 68.0 \| 476 \| 1.8796 \| 0.2675 \| 0.8711 \| 5.9457 \| 0.2675 \| 0.2470 \| 0.2100 \| 0.5737 \| \| No log \| 69.0 \| 483 \| 1.8635 \| 0.2675 \| 0.8721 \| 5.9312 \| 0.2675 \| 0.2493 \| 0.1788 \| 0.5751 \| \| No log \| 70.0 \| 490 \| 1.8801 \| 0.2625 \| 0.8710 \| 5.9629 \| 0.2625 \| 0.2467 \| 0.1974 \| 0.5721 \| \| No log \| 71.0 \| 497 \| 1.8936 \| 0.26 \| 0.8791 \| 6.0358 \| 0.26 \| 0.2481 \| 0.1922 \| 0.5844 \| \| 0.9216 \| 72.0 \| 504 \| 1.8736 \| 0.275 \| 0.8715 \| 6.0493 \| 0.275 \| 0.2569 \| 0.2099 \| 0.5710 \| \| 0.9216 \| 73.0 \| 511 \| 1.8784 \| 0.2525 \| 0.8760 \| 6.1441 \| 0.2525 \| 0.2401 \| 0.1978 \| 0.5849 \| \| 0.9216 \| 74.0 \| 518 \| 1.8843 \| 0.2725 \| 0.8763 \| 6.1948 \| 0.2725 \| 0.2533 \| 0.2007 \| 0.5801 \| \| 0.9216 \| 75.0 \| 525 \| 1.8785 \| 0.2675 \| 0.8784 \| 5.9868 \| 0.2675 \| 0.2578 \| 0.1975 \| 0.5851 \| \| 0.9216 \| 76.0 \| 532 \| 1.8812 \| 0.275 \| 0.8725 \| 5.9367 \| 0.275 \| 0.2594 \| 0.2037 \| 0.5744 \| \| 0.9216 \| 77.0 \| 539 \| 1.8956 \| 0.27 \| 0.8746 \| 5.9038 \| 0.27 \| 0.2541 \| 0.1816 \| 0.5738 \| \| 0.9216 \| 78.0 \| 546 \| 1.8897 \| 0.265 \| 0.8802 \| 5.9763 \| 0.265 \| 0.2493 \| 0.2098 \| 0.5866 \| \| 0.9216 \| 79.0 \| 553 \| 1.8728 \| 0.275 \| 0.8752 \| 6.0806 \| 0.275 \| 0.2623 \| 0.1874 \| 0.5794 \| \| 0.9216 \| 80.0 \| 560 \| 1.8887 \| 0.2725 \| 0.8759 \| 6.2762 \| 0.2725 \| 0.2520 \| 0.2005 \| 0.5768 \| \| 0.9216 \| 81.0 \| 567 \| 1.8987 \| 0.2725 \| 0.8787 \| 6.2444 \| 0.2725 \| 0.2587 \| 0.2183 \| 0.5773 \| \| 0.9216 \| 82.0 \| 574 \| 1.8759 \| 0.2625 \| 0.8773 \| 6.1643 \| 0.2625 \| 0.2541 \| 0.1922 \| 0.5805 \| \| 0.9216 \| 83.0 \| 581 \| 1.8766 \| 0.27 \| 0.8748 \| 6.0036 \| 0.27 \| 0.2554 \| 0.1784 \| 0.5762 \| \| 0.9216 \| 84.0 \| 588 \| 1.8809 \| 0.2625 \| 0.8764 \| 6.0488 \| 0.2625 \| 0.2469 \| 0.2030 \| 0.5833 \| \| 0.9216 \| 85.0 \| 595 \| 1.8982 \| 0.26 \| 0.8775 \| 6.0747 \| 0.26 \| 0.2453 \| 0.1998 \| 0.5851 \| \| 0.9216 \| 86.0 \| 602 \| 1.8912 \| 0.27 \| 0.8798 \| 6.1894 \| 0.27 \| 0.2566 \| 0.1938 \| 0.5839 \| \| 0.9216 \| 87.0 \| 609 \| 1.8847 \| 0.2775 \| 0.8769 \| 6.2744 \| 0.2775 \| 0.2643 \| 0.2019 \| 0.5775 \| \| 0.9216 \| 88.0 \| 616 \| 1.8734 \| 0.265 \| 0.8741 \| 6.1928 \| 0.265 \| 0.2526 \| 0.1763 \| 0.5820 \| \| 0.9216 \| 89.0 \| 623 \| 1.8760 \| 0.2725 \| 0.8768 \| 6.0274 \| 0.2725 \| 0.2620 \| 0.2039 \| 0.5792 \| \| 0.9216 \| 90.0 \| 630 \| 1.8860 \| 0.265 \| 0.8771 \| 6.0912 \| 0.265 \| 0.2518 \| 0.1924 \| 0.5810 \| \| 0.9216 \| 91.0 \| 637 \| 1.8865 \| 0.2625 \| 0.8750 \| 6.2350 \| 0.2625 \| 0.2476 \| 0.1844 \| 0.5791 \| \| 0.9216 \| 92.0 \| 644 \| 1.8815 \| 0.2725 \| 0.8733 \| 6.0962 \| 0.2725 \| 0.2563 \| 0.2013 \| 0.5721 \| \| 0.9216 \| 93.0 \| 651 \| 1.8794 \| 0.27 \| 0.8756 \| 6.2535 \| 0.27 \| 0.2562 \| 0.2028 \| 0.5764 \| \| 0.9216 \| 94.0 \| 658 \| 1.8835 \| 0.2675 \| 0.8769 \| 6.2039 \| 0.2675 \| 0.2562 \| 0.1928 \| 0.5773 \| \| 0.9216 \| 95.0 \| 665 \| 1.8904 \| 0.27 \| 0.8786 \| 6.1504 \| 0.27 \| 0.2543 \| 0.2034 \| 0.5768 \| \| 0.9216 \| 96.0 \| 672 \| 1.8911 \| 0.26 \| 0.8788 \| 6.1527 \| 0.26 \| 0.2465 \| 0.2025 \| 0.5829 \| \| 0.9216 \| 97.0 \| 679 \| 1.8871 \| 0.265 \| 0.8776 \| 6.0994 \| 0.265 \| 0.2519 \| 0.2126 \| 0.5794 \| \| 0.9216 \| 98.0 \| 686 \| 1.8825 \| 0.265 \| 0.8769 \| 6.1564 \| 0.265 \| 0.2516 \| 0.1987 \| 0.5776 \| \| 0.9216 \| 99.0 \| 693 \| 1.8803 \| 0.2675 \| 0.8766 \| 6.1183 \| 0.2675 \| 0.2561 \| 0.2095 \| 0.5798 \| \| 0.9216 \| 100.0 \| 700 \| 1.8796 \| 0.26 \| 0.8768 \| 6.0962 \| 0.26 \| 0.2480 \| 0.2002 \| 0.5815 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2
HeinrichWirth/taxi	HeinrichWirth	2023-07-10T11:44:14Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T11:43:46Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="HeinrichWirth/taxi", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
tyavika/LR1E4-BS16-Distil-CNN512LSTM256NoBi	tyavika	2023-07-10T11:29:13Z	76	0	transformers	[ "transformers", "pytorch", "distilbert", "question-answering", "generated_from_trainer", "endpoints_compatible", "region:us" ]	question-answering	2023-07-09T20:24:08Z	--- tags: - generated_from_trainer model-index: - name: LR1E4-BS16-Distil-CNN512LSTM256NoBi results: [] --- <!-- 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. --> # LR1E4-BS16-Distil-CNN512LSTM256NoBi This model is a fine-tuned version of [tyavika/LR1E4-BS16-Distil-CNN512LSTM256NoBi](https://huggingface.co/tyavika/LR1E4-BS16-Distil-CNN512LSTM256NoBi) on the None 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: 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: 10 ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
Junr-syl/tweet_sentiments_analysis	Junr-syl	2023-07-10T11:21:39Z	162	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-07T09:19:12Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: tweet_sentiments_analysis results: [] --- <!-- 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. --> # tweet_sentiments_analysis 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: - eval_loss: 0.3953 - eval_accuracy: 0.8660 - eval_runtime: 254.1512 - eval_samples_per_second: 31.473 - eval_steps_per_second: 3.935 - step: 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: 5e-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: 1000 ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
jordyvl/dit-base_tobacco_small_student	jordyvl	2023-07-10T10:58:25Z	161	0	transformers	[ "transformers", "pytorch", "tensorboard", "beit", "image-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-10T10:07:51Z	--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: dit-base_tobacco_small_student results: [] --- <!-- 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. --> # dit-base_tobacco_small_student This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.3305 - Accuracy: 0.435 - Brier Loss: 1.0472 - Nll: 10.3327 - F1 Micro: 0.435 - F1 Macro: 0.4299 - Ece: 0.5115 - Aurc: 0.4245 ## 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: 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: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:-------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 50 \| 2.1780 \| 0.16 \| 0.8745 \| 11.2696 \| 0.16 \| 0.0323 \| 0.2326 \| 0.8208 \| \| No log \| 2.0 \| 100 \| 2.1761 \| 0.19 \| 0.8727 \| 10.5065 \| 0.19 \| 0.0548 \| 0.2712 \| 0.7980 \| \| No log \| 3.0 \| 150 \| 2.1426 \| 0.16 \| 0.8689 \| 8.8915 \| 0.16 \| 0.0451 \| 0.2697 \| 0.6322 \| \| No log \| 4.0 \| 200 \| 2.0668 \| 0.225 \| 0.8434 \| 9.6036 \| 0.225 \| 0.1219 \| 0.2680 \| 0.6623 \| \| No log \| 5.0 \| 250 \| 2.0633 \| 0.21 \| 0.8447 \| 5.7679 \| 0.2100 \| 0.1401 \| 0.2733 \| 0.5765 \| \| No log \| 6.0 \| 300 \| 2.0030 \| 0.22 \| 0.8351 \| 7.1501 \| 0.22 \| 0.1132 \| 0.3000 \| 0.6750 \| \| No log \| 7.0 \| 350 \| 1.9273 \| 0.32 \| 0.8243 \| 6.2911 \| 0.32 \| 0.2612 \| 0.2822 \| 0.6549 \| \| No log \| 8.0 \| 400 \| 1.7954 \| 0.365 \| 0.7742 \| 4.2641 \| 0.3650 \| 0.2647 \| 0.2630 \| 0.5031 \| \| No log \| 9.0 \| 450 \| 1.8070 \| 0.36 \| 0.7720 \| 4.9274 \| 0.36 \| 0.2914 \| 0.2601 \| 0.4871 \| \| 1.9795 \| 10.0 \| 500 \| 1.7838 \| 0.34 \| 0.7857 \| 3.3860 \| 0.34 \| 0.2387 \| 0.2902 \| 0.5057 \| \| 1.9795 \| 11.0 \| 550 \| 1.7214 \| 0.395 \| 0.7404 \| 4.1630 \| 0.395 \| 0.2995 \| 0.2922 \| 0.4210 \| \| 1.9795 \| 12.0 \| 600 \| 1.6834 \| 0.445 \| 0.7284 \| 3.7081 \| 0.445 \| 0.3444 \| 0.2700 \| 0.3914 \| \| 1.9795 \| 13.0 \| 650 \| 1.6992 \| 0.38 \| 0.7641 \| 4.1246 \| 0.38 \| 0.3045 \| 0.3375 \| 0.4155 \| \| 1.9795 \| 14.0 \| 700 \| 1.8695 \| 0.395 \| 0.7711 \| 5.6899 \| 0.395 \| 0.3432 \| 0.3224 \| 0.4425 \| \| 1.9795 \| 15.0 \| 750 \| 1.8757 \| 0.38 \| 0.7939 \| 5.1099 \| 0.38 \| 0.3879 \| 0.3102 \| 0.4313 \| \| 1.9795 \| 16.0 \| 800 \| 2.0457 \| 0.405 \| 0.8184 \| 5.6034 \| 0.405 \| 0.3957 \| 0.3256 \| 0.4414 \| \| 1.9795 \| 17.0 \| 850 \| 2.2243 \| 0.395 \| 0.8653 \| 7.7124 \| 0.395 \| 0.3567 \| 0.3887 \| 0.3997 \| \| 1.9795 \| 18.0 \| 900 \| 1.9309 \| 0.45 \| 0.7794 \| 5.2698 \| 0.45 \| 0.3763 \| 0.3626 \| 0.3767 \| \| 1.9795 \| 19.0 \| 950 \| 2.2285 \| 0.415 \| 0.8319 \| 6.7127 \| 0.415 \| 0.4153 \| 0.3667 \| 0.3942 \| \| 0.6717 \| 20.0 \| 1000 \| 2.3745 \| 0.445 \| 0.8643 \| 7.4432 \| 0.445 \| 0.4290 \| 0.3859 \| 0.4046 \| \| 0.6717 \| 21.0 \| 1050 \| 2.5389 \| 0.41 \| 0.9148 \| 7.6865 \| 0.41 \| 0.3994 \| 0.4351 \| 0.4054 \| \| 0.6717 \| 22.0 \| 1100 \| 2.5537 \| 0.465 \| 0.8500 \| 8.1266 \| 0.465 \| 0.4623 \| 0.4070 \| 0.3900 \| \| 0.6717 \| 23.0 \| 1150 \| 2.8355 \| 0.42 \| 0.9426 \| 8.8542 \| 0.4200 \| 0.3930 \| 0.4508 \| 0.4201 \| \| 0.6717 \| 24.0 \| 1200 \| 2.8575 \| 0.4 \| 0.9962 \| 7.6428 \| 0.4000 \| 0.3502 \| 0.4994 \| 0.4119 \| \| 0.6717 \| 25.0 \| 1250 \| 2.8704 \| 0.445 \| 0.9418 \| 9.2600 \| 0.445 \| 0.4570 \| 0.4309 \| 0.4021 \| \| 0.6717 \| 26.0 \| 1300 \| 3.4702 \| 0.43 \| 0.9641 \| 12.1621 \| 0.4300 \| 0.3977 \| 0.4590 \| 0.3597 \| \| 0.6717 \| 27.0 \| 1350 \| 3.1484 \| 0.475 \| 0.9518 \| 8.1474 \| 0.4750 \| 0.4641 \| 0.4809 \| 0.4088 \| \| 0.6717 \| 28.0 \| 1400 \| 3.2299 \| 0.455 \| 0.9673 \| 9.6161 \| 0.455 \| 0.4205 \| 0.4711 \| 0.3806 \| \| 0.6717 \| 29.0 \| 1450 \| 3.4968 \| 0.425 \| 1.0136 \| 10.5614 \| 0.425 \| 0.3992 \| 0.4743 \| 0.3773 \| \| 0.0268 \| 30.0 \| 1500 \| 3.1340 \| 0.46 \| 0.9443 \| 8.5023 \| 0.46 \| 0.4296 \| 0.4557 \| 0.3735 \| \| 0.0268 \| 31.0 \| 1550 \| 3.4297 \| 0.435 \| 1.0058 \| 8.2428 \| 0.435 \| 0.3979 \| 0.4967 \| 0.3848 \| \| 0.0268 \| 32.0 \| 1600 \| 3.6922 \| 0.4 \| 1.0488 \| 10.8019 \| 0.4000 \| 0.3880 \| 0.5223 \| 0.4017 \| \| 0.0268 \| 33.0 \| 1650 \| 3.6009 \| 0.445 \| 0.9964 \| 10.1007 \| 0.445 \| 0.4204 \| 0.4924 \| 0.3981 \| \| 0.0268 \| 34.0 \| 1700 \| 3.6678 \| 0.425 \| 1.0494 \| 9.1369 \| 0.425 \| 0.3896 \| 0.5159 \| 0.4192 \| \| 0.0268 \| 35.0 \| 1750 \| 3.5743 \| 0.45 \| 0.9953 \| 9.5996 \| 0.45 \| 0.4182 \| 0.4934 \| 0.4030 \| \| 0.0268 \| 36.0 \| 1800 \| 3.5551 \| 0.465 \| 0.9877 \| 9.6080 \| 0.465 \| 0.4221 \| 0.5033 \| 0.3977 \| \| 0.0268 \| 37.0 \| 1850 \| 3.7424 \| 0.435 \| 1.0191 \| 9.9258 \| 0.435 \| 0.4292 \| 0.4955 \| 0.4120 \| \| 0.0268 \| 38.0 \| 1900 \| 3.7093 \| 0.45 \| 1.0051 \| 9.7038 \| 0.45 \| 0.4033 \| 0.4966 \| 0.3857 \| \| 0.0268 \| 39.0 \| 1950 \| 3.7240 \| 0.45 \| 1.0076 \| 9.8462 \| 0.45 \| 0.4027 \| 0.4953 \| 0.3962 \| \| 0.0022 \| 40.0 \| 2000 \| 3.7503 \| 0.455 \| 1.0090 \| 9.9967 \| 0.455 \| 0.4076 \| 0.5056 \| 0.3968 \| \| 0.0022 \| 41.0 \| 2050 \| 3.5545 \| 0.44 \| 1.0007 \| 8.7616 \| 0.44 \| 0.4285 \| 0.4894 \| 0.4008 \| \| 0.0022 \| 42.0 \| 2100 \| 3.7452 \| 0.435 \| 1.0142 \| 9.4376 \| 0.435 \| 0.4135 \| 0.5032 \| 0.4022 \| \| 0.0022 \| 43.0 \| 2150 \| 3.5980 \| 0.47 \| 0.9532 \| 8.2333 \| 0.47 \| 0.4441 \| 0.4650 \| 0.4113 \| \| 0.0022 \| 44.0 \| 2200 \| 3.7055 \| 0.45 \| 0.9946 \| 9.0121 \| 0.45 \| 0.4327 \| 0.4817 \| 0.3688 \| \| 0.0022 \| 45.0 \| 2250 \| 3.8500 \| 0.435 \| 1.0161 \| 9.2035 \| 0.435 \| 0.4164 \| 0.5128 \| 0.3723 \| \| 0.0022 \| 46.0 \| 2300 \| 3.8806 \| 0.435 \| 1.0261 \| 10.7033 \| 0.435 \| 0.4323 \| 0.5008 \| 0.3812 \| \| 0.0022 \| 47.0 \| 2350 \| 3.8114 \| 0.455 \| 1.0128 \| 9.6784 \| 0.455 \| 0.4236 \| 0.5025 \| 0.3873 \| \| 0.0022 \| 48.0 \| 2400 \| 3.8743 \| 0.435 \| 1.0294 \| 8.7193 \| 0.435 \| 0.3734 \| 0.5109 \| 0.3783 \| \| 0.0022 \| 49.0 \| 2450 \| 3.9281 \| 0.43 \| 1.0414 \| 9.9489 \| 0.4300 \| 0.4296 \| 0.5047 \| 0.4049 \| \| 0.0047 \| 50.0 \| 2500 \| 3.7824 \| 0.45 \| 0.9956 \| 10.7814 \| 0.45 \| 0.4467 \| 0.4975 \| 0.3949 \| \| 0.0047 \| 51.0 \| 2550 \| 4.0089 \| 0.475 \| 0.9668 \| 11.9005 \| 0.4750 \| 0.4253 \| 0.4637 \| 0.4501 \| \| 0.0047 \| 52.0 \| 2600 \| 3.7248 \| 0.43 \| 0.9909 \| 10.6449 \| 0.4300 \| 0.4064 \| 0.4750 \| 0.4023 \| \| 0.0047 \| 53.0 \| 2650 \| 3.7911 \| 0.415 \| 1.0491 \| 9.1188 \| 0.415 \| 0.3608 \| 0.5130 \| 0.4173 \| \| 0.0047 \| 54.0 \| 2700 \| 3.6925 \| 0.44 \| 1.0000 \| 8.9655 \| 0.44 \| 0.3970 \| 0.4826 \| 0.4168 \| \| 0.0047 \| 55.0 \| 2750 \| 3.6214 \| 0.46 \| 0.9590 \| 9.5422 \| 0.46 \| 0.4440 \| 0.4636 \| 0.3829 \| \| 0.0047 \| 56.0 \| 2800 \| 4.3545 \| 0.405 \| 1.0811 \| 10.6531 \| 0.405 \| 0.4090 \| 0.5439 \| 0.4533 \| \| 0.0047 \| 57.0 \| 2850 \| 3.6835 \| 0.46 \| 0.9717 \| 8.2408 \| 0.46 \| 0.4367 \| 0.4950 \| 0.4118 \| \| 0.0047 \| 58.0 \| 2900 \| 4.0080 \| 0.465 \| 1.0011 \| 9.3764 \| 0.465 \| 0.4579 \| 0.4927 \| 0.4234 \| \| 0.0047 \| 59.0 \| 2950 \| 4.0141 \| 0.45 \| 1.0014 \| 9.7100 \| 0.45 \| 0.4443 \| 0.4987 \| 0.4220 \| \| 0.0118 \| 60.0 \| 3000 \| 3.7963 \| 0.43 \| 1.0135 \| 9.4040 \| 0.4300 \| 0.4007 \| 0.5007 \| 0.3979 \| \| 0.0118 \| 61.0 \| 3050 \| 4.0609 \| 0.43 \| 1.0426 \| 9.3533 \| 0.4300 \| 0.3825 \| 0.5266 \| 0.4285 \| \| 0.0118 \| 62.0 \| 3100 \| 4.0150 \| 0.47 \| 1.0002 \| 9.3307 \| 0.47 \| 0.4490 \| 0.5030 \| 0.4052 \| \| 0.0118 \| 63.0 \| 3150 \| 3.7982 \| 0.47 \| 0.9660 \| 8.5060 \| 0.47 \| 0.4581 \| 0.4716 \| 0.3988 \| \| 0.0118 \| 64.0 \| 3200 \| 4.3553 \| 0.44 \| 1.0428 \| 10.3840 \| 0.44 \| 0.4218 \| 0.5163 \| 0.4312 \| \| 0.0118 \| 65.0 \| 3250 \| 3.7142 \| 0.44 \| 0.9900 \| 8.5049 \| 0.44 \| 0.4298 \| 0.4849 \| 0.3735 \| \| 0.0118 \| 66.0 \| 3300 \| 3.7411 \| 0.47 \| 0.9661 \| 8.1935 \| 0.47 \| 0.4497 \| 0.4789 \| 0.3812 \| \| 0.0118 \| 67.0 \| 3350 \| 3.7858 \| 0.49 \| 0.9574 \| 8.8397 \| 0.49 \| 0.4799 \| 0.4616 \| 0.3895 \| \| 0.0118 \| 68.0 \| 3400 \| 3.7927 \| 0.495 \| 0.9459 \| 8.6915 \| 0.495 \| 0.4870 \| 0.4577 \| 0.3883 \| \| 0.0118 \| 69.0 \| 3450 \| 3.8348 \| 0.5 \| 0.9454 \| 8.8298 \| 0.5 \| 0.4889 \| 0.4715 \| 0.3891 \| \| 0.0004 \| 70.0 \| 3500 \| 3.8551 \| 0.48 \| 0.9500 \| 8.9827 \| 0.48 \| 0.4755 \| 0.4691 \| 0.3913 \| \| 0.0004 \| 71.0 \| 3550 \| 3.8432 \| 0.48 \| 0.9622 \| 9.1404 \| 0.48 \| 0.4691 \| 0.4690 \| 0.3885 \| \| 0.0004 \| 72.0 \| 3600 \| 3.8594 \| 0.48 \| 0.9617 \| 8.8182 \| 0.48 \| 0.4691 \| 0.4805 \| 0.3854 \| \| 0.0004 \| 73.0 \| 3650 \| 3.8855 \| 0.485 \| 0.9622 \| 8.8248 \| 0.485 \| 0.4760 \| 0.4809 \| 0.3881 \| \| 0.0004 \| 74.0 \| 3700 \| 3.8996 \| 0.49 \| 0.9610 \| 8.9750 \| 0.49 \| 0.4818 \| 0.4634 \| 0.3892 \| \| 0.0004 \| 75.0 \| 3750 \| 3.9921 \| 0.475 \| 0.9642 \| 9.5409 \| 0.4750 \| 0.4597 \| 0.4666 \| 0.4185 \| \| 0.0004 \| 76.0 \| 3800 \| 4.1128 \| 0.43 \| 1.0429 \| 9.9966 \| 0.4300 \| 0.3844 \| 0.5187 \| 0.4056 \| \| 0.0004 \| 77.0 \| 3850 \| 4.0783 \| 0.44 \| 1.0172 \| 9.3016 \| 0.44 \| 0.4205 \| 0.5051 \| 0.3988 \| \| 0.0004 \| 78.0 \| 3900 \| 4.0804 \| 0.445 \| 1.0254 \| 8.9753 \| 0.445 \| 0.4246 \| 0.5089 \| 0.3982 \| \| 0.0004 \| 79.0 \| 3950 \| 4.0892 \| 0.445 \| 1.0269 \| 8.8290 \| 0.445 \| 0.4246 \| 0.5069 \| 0.4000 \| \| 0.0002 \| 80.0 \| 4000 \| 4.1013 \| 0.445 \| 1.0258 \| 9.1363 \| 0.445 \| 0.4246 \| 0.5129 \| 0.4033 \| \| 0.0002 \| 81.0 \| 4050 \| 4.0985 \| 0.44 \| 1.0287 \| 9.1459 \| 0.44 \| 0.4213 \| 0.5074 \| 0.4054 \| \| 0.0002 \| 82.0 \| 4100 \| 4.1029 \| 0.44 \| 1.0263 \| 9.3107 \| 0.44 \| 0.4211 \| 0.5125 \| 0.4066 \| \| 0.0002 \| 83.0 \| 4150 \| 4.1075 \| 0.44 \| 1.0248 \| 9.4604 \| 0.44 \| 0.4224 \| 0.5164 \| 0.4061 \| \| 0.0002 \| 84.0 \| 4200 \| 4.1087 \| 0.44 \| 1.0225 \| 9.7739 \| 0.44 \| 0.4221 \| 0.5090 \| 0.4055 \| \| 0.0002 \| 85.0 \| 4250 \| 4.1248 \| 0.44 \| 1.0262 \| 9.7747 \| 0.44 \| 0.4259 \| 0.5032 \| 0.4065 \| \| 0.0002 \| 86.0 \| 4300 \| 4.1527 \| 0.445 \| 1.0263 \| 9.4647 \| 0.445 \| 0.4299 \| 0.5128 \| 0.4066 \| \| 0.0002 \| 87.0 \| 4350 \| 4.0529 \| 0.475 \| 0.9810 \| 9.1439 \| 0.4750 \| 0.4488 \| 0.4910 \| 0.3938 \| \| 0.0002 \| 88.0 \| 4400 \| 4.1405 \| 0.455 \| 1.0091 \| 9.5149 \| 0.455 \| 0.4230 \| 0.4966 \| 0.4147 \| \| 0.0002 \| 89.0 \| 4450 \| 4.3483 \| 0.41 \| 1.0724 \| 9.8421 \| 0.41 \| 0.4083 \| 0.5384 \| 0.4090 \| \| 0.0008 \| 90.0 \| 4500 \| 4.5574 \| 0.39 \| 1.1077 \| 11.2517 \| 0.39 \| 0.3940 \| 0.5618 \| 0.4405 \| \| 0.0008 \| 91.0 \| 4550 \| 4.5104 \| 0.41 \| 1.0890 \| 10.8687 \| 0.41 \| 0.4173 \| 0.5411 \| 0.4350 \| \| 0.0008 \| 92.0 \| 4600 \| 4.3791 \| 0.425 \| 1.0672 \| 10.7198 \| 0.425 \| 0.4202 \| 0.5233 \| 0.4306 \| \| 0.0008 \| 93.0 \| 4650 \| 4.3608 \| 0.43 \| 1.0553 \| 10.8428 \| 0.4300 \| 0.4236 \| 0.5196 \| 0.4284 \| \| 0.0008 \| 94.0 \| 4700 \| 4.3469 \| 0.44 \| 1.0474 \| 10.6774 \| 0.44 \| 0.4428 \| 0.5020 \| 0.4280 \| \| 0.0008 \| 95.0 \| 4750 \| 4.3420 \| 0.44 \| 1.0487 \| 10.5138 \| 0.44 \| 0.4385 \| 0.5260 \| 0.4270 \| \| 0.0008 \| 96.0 \| 4800 \| 4.3385 \| 0.435 \| 1.0491 \| 10.3448 \| 0.435 \| 0.4312 \| 0.5170 \| 0.4266 \| \| 0.0008 \| 97.0 \| 4850 \| 4.3341 \| 0.435 \| 1.0485 \| 10.3378 \| 0.435 \| 0.4312 \| 0.5136 \| 0.4261 \| \| 0.0008 \| 98.0 \| 4900 \| 4.3336 \| 0.435 \| 1.0480 \| 10.3350 \| 0.435 \| 0.4312 \| 0.5184 \| 0.4253 \| \| 0.0008 \| 99.0 \| 4950 \| 4.3306 \| 0.435 \| 1.0472 \| 10.3328 \| 0.435 \| 0.4299 \| 0.5116 \| 0.4245 \| \| 0.0001 \| 100.0 \| 5000 \| 4.3305 \| 0.435 \| 1.0472 \| 10.3327 \| 0.435 \| 0.4299 \| 0.5115 \| 0.4245 \| ### Framework versions - Transformers 4.28.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.12.0 - Tokenizers 0.12.1
km0228kr/xlm-roberta-base-finetuned-panx-de	km0228kr	2023-07-10T10:57:14Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-07-10T10:47:06Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.de split: validation args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8653353814644136 --- <!-- 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. --> # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1339 - F1: 0.8653 ## 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: 24 - eval_batch_size: 24 - 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 \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2583 \| 1.0 \| 525 \| 0.1596 \| 0.8231 \| \| 0.1262 \| 2.0 \| 1050 \| 0.1395 \| 0.8468 \| \| 0.0824 \| 3.0 \| 1575 \| 0.1339 \| 0.8653 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
Apocalypse-19/trocr-MICR	Apocalypse-19	2023-07-10T10:45:25Z	73	1	transformers	[ "transformers", "pytorch", "vision-encoder-decoder", "image-text-to-text", "Image-to-Text", "trocr", "en", "endpoints_compatible", "region:us" ]	image-text-to-text	2023-07-07T15:35:59Z	--- language: - en tags: - Image-to-Text - trocr --- ## TrOCR-MICR An OCR model for transcribing e13b MICR codes. Finetuned from Microsoft's [TrOCR-large-stage1](https://huggingface.co/microsoft/trocr-large-printed). Model was finetuned using the e13b portion of the MICR dataset given [here](https://github.com/DoubangoTelecom/tesseractMICR)
FelixChao/medical_faq_gpt_vicuna7b_chinese	FelixChao	2023-07-10T10:32:00Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-10T10:31:43Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0.dev0
crisU8/bert-finetuned-ner-clinical-plncmm-large-25	crisU8	2023-07-10T10:18:28Z	110	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-07-10T09:52:07Z	--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner-clinical-plncmm-large-25 results: [] --- <!-- 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-clinical-plncmm-large-25 This model is a fine-tuned version of [plncmm/beto-clinical-wl-es](https://huggingface.co/plncmm/beto-clinical-wl-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2487 - Precision: 0.7372 - Recall: 0.8035 - F1: 0.7689 - Accuracy: 0.9270 ## 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: 18 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 400 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 446 \| 0.2607 \| 0.6701 \| 0.7772 \| 0.7197 \| 0.9113 \| \| 0.6128 \| 2.0 \| 892 \| 0.2298 \| 0.7266 \| 0.7964 \| 0.7599 \| 0.9254 \| \| 0.1927 \| 3.0 \| 1338 \| 0.2487 \| 0.7372 \| 0.8035 \| 0.7689 \| 0.9270 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
jordyvl/dit-tiny_rvl_cdip_100_examples_per_class_kd_MSE_lr_fix	jordyvl	2023-07-10T10:05:07Z	161	0	transformers	[ "transformers", "pytorch", "tensorboard", "beit", "image-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-10T09:48:49Z	--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: dit-tiny_rvl_cdip_100_examples_per_class_kd_MSE_lr_fix results: [] --- <!-- 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. --> # dit-tiny_rvl_cdip_100_examples_per_class_kd_MSE_lr_fix This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4358 - Accuracy: 0.195 - Brier Loss: 0.9035 - Nll: 12.0550 - F1 Micro: 0.195 - F1 Macro: 0.1471 - Ece: 0.1675 - Aurc: 0.6988 ## 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: 32 - eval_batch_size: 32 - 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: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:-------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 25 \| 1.5167 \| 0.07 \| 0.9368 \| 20.8948 \| 0.07 \| 0.0305 \| 0.1106 \| 0.8850 \| \| No log \| 2.0 \| 50 \| 1.5246 \| 0.08 \| 0.9362 \| 21.4368 \| 0.08 \| 0.0346 \| 0.1200 \| 0.8659 \| \| No log \| 3.0 \| 75 \| 1.5053 \| 0.1 \| 0.9340 \| 23.7241 \| 0.1000 \| 0.0522 \| 0.1280 \| 0.8087 \| \| No log \| 4.0 \| 100 \| 1.5097 \| 0.0975 \| 0.9322 \| 17.3004 \| 0.0975 \| 0.0487 \| 0.1220 \| 0.8220 \| \| No log \| 5.0 \| 125 \| 1.4926 \| 0.12 \| 0.9296 \| 16.3893 \| 0.12 \| 0.0600 \| 0.1284 \| 0.7752 \| \| No log \| 6.0 \| 150 \| 1.4838 \| 0.105 \| 0.9273 \| 19.3692 \| 0.1050 \| 0.0356 \| 0.1254 \| 0.7955 \| \| No log \| 7.0 \| 175 \| 1.4729 \| 0.0975 \| 0.9229 \| 18.6899 \| 0.0975 \| 0.0411 \| 0.1134 \| 0.7963 \| \| No log \| 8.0 \| 200 \| 1.4754 \| 0.125 \| 0.9196 \| 17.7842 \| 0.125 \| 0.0676 \| 0.1238 \| 0.7778 \| \| No log \| 9.0 \| 225 \| 1.4725 \| 0.1125 \| 0.9193 \| 16.6572 \| 0.1125 \| 0.0505 \| 0.1254 \| 0.7839 \| \| No log \| 10.0 \| 250 \| 1.4702 \| 0.1175 \| 0.9168 \| 16.3975 \| 0.1175 \| 0.0556 \| 0.1183 \| 0.7638 \| \| No log \| 11.0 \| 275 \| 1.4648 \| 0.1175 \| 0.9169 \| 18.4274 \| 0.1175 \| 0.0558 \| 0.1219 \| 0.7806 \| \| No log \| 12.0 \| 300 \| 1.4660 \| 0.155 \| 0.9166 \| 15.6492 \| 0.155 \| 0.0791 \| 0.1411 \| 0.7512 \| \| No log \| 13.0 \| 325 \| 1.4684 \| 0.16 \| 0.9164 \| 17.1698 \| 0.16 \| 0.1140 \| 0.1519 \| 0.7285 \| \| No log \| 14.0 \| 350 \| 1.4662 \| 0.1175 \| 0.9158 \| 17.6999 \| 0.1175 \| 0.0501 \| 0.1269 \| 0.7637 \| \| No log \| 15.0 \| 375 \| 1.4602 \| 0.1675 \| 0.9143 \| 13.2540 \| 0.1675 \| 0.1153 \| 0.1515 \| 0.7223 \| \| No log \| 16.0 \| 400 \| 1.4556 \| 0.1325 \| 0.9138 \| 13.3868 \| 0.1325 \| 0.0881 \| 0.1323 \| 0.7558 \| \| No log \| 17.0 \| 425 \| 1.4527 \| 0.175 \| 0.9128 \| 11.1983 \| 0.175 \| 0.1334 \| 0.1596 \| 0.7153 \| \| No log \| 18.0 \| 450 \| 1.4535 \| 0.1625 \| 0.9111 \| 17.6046 \| 0.1625 \| 0.1021 \| 0.1435 \| 0.7379 \| \| No log \| 19.0 \| 475 \| 1.4453 \| 0.1825 \| 0.9086 \| 11.8948 \| 0.1825 \| 0.1228 \| 0.1594 \| 0.7098 \| \| 1.4614 \| 20.0 \| 500 \| 1.4431 \| 0.1525 \| 0.9078 \| 14.2631 \| 0.1525 \| 0.1115 \| 0.1410 \| 0.7293 \| \| 1.4614 \| 21.0 \| 525 \| 1.4392 \| 0.1825 \| 0.9063 \| 10.7664 \| 0.1825 \| 0.1378 \| 0.1567 \| 0.7058 \| \| 1.4614 \| 22.0 \| 550 \| 1.4469 \| 0.1775 \| 0.9055 \| 13.4724 \| 0.1775 \| 0.1212 \| 0.1483 \| 0.7107 \| \| 1.4614 \| 23.0 \| 575 \| 1.4356 \| 0.17 \| 0.9039 \| 11.8141 \| 0.17 \| 0.1232 \| 0.1515 \| 0.7091 \| \| 1.4614 \| 24.0 \| 600 \| 1.4370 \| 0.1875 \| 0.9039 \| 12.9338 \| 0.1875 \| 0.1384 \| 0.1539 \| 0.7017 \| \| 1.4614 \| 25.0 \| 625 \| 1.4358 \| 0.195 \| 0.9035 \| 12.0550 \| 0.195 \| 0.1471 \| 0.1675 \| 0.6988 \| ### Framework versions - Transformers 4.28.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.12.0 - Tokenizers 0.12.1
ducnapa/cute-cartoon-illustration	ducnapa	2023-07-10T09:59:49Z	29	5	diffusers	[ "diffusers", "text-to-image", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-06-23T10:00:04Z	--- library_name: diffusers pipeline_tag: text-to-image ---
justinpinkney/falcon-7b	justinpinkney	2023-07-10T09:49:02Z	13	0	transformers	[ "transformers", "pytorch", "RefinedWebModel", "text-generation", "custom_code", "en", "dataset:tiiuae/falcon-refinedweb", "arxiv:2205.14135", "arxiv:1911.02150", "arxiv:2101.00027", "arxiv:2005.14165", "arxiv:2104.09864", "arxiv:2306.01116", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2023-07-07T14:25:17Z	--- datasets: - tiiuae/falcon-refinedweb language: - en inference: false license: apache-2.0 duplicated_from: tiiuae/falcon-7b --- # 🚀 Falcon-7B Falcon-7B is a 7B parameters causal decoder-only model built by [TII](https://www.tii.ae) and trained on 1,500B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) enhanced with curated corpora. It is made available under the Apache 2.0 license. Paper coming soon 😊. 🤗 To get started with Falcon (inference, finetuning, quantization, etc.), we recommend reading [this great blogpost fron HF](https://huggingface.co/blog/falcon)! ## Why use Falcon-7B? * It outperforms comparable open-source models (e.g., [MPT-7B](https://huggingface.co/mosaicml/mpt-7b), [StableLM](https://github.com/Stability-AI/StableLM), [RedPajama](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-7B-v0.1) etc.), thanks to being trained on 1,500B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) enhanced with curated corpora. See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). * It features an architecture optimized for inference, with FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)) and multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)). * It is made available under a permissive Apache 2.0 license allowing for commercial use, without any royalties or restrictions. ⚠️ This is a raw, pretrained model, which should be further finetuned for most usecases. If you are looking for a version better suited to taking generic instructions in a chat format, we recommend taking a look at [Falcon-7B-Instruct](https://huggingface.co/tiiuae/falcon-7b-instruct). 🔥 Looking for an even more powerful model? [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b) is Falcon-7B's big brother! ```python from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model = "tiiuae/falcon-7b" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, tokenizer=tokenizer, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", ) sequences = pipeline( "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:", max_length=200, do_sample=True, top_k=10, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ``` 💥 Falcon LLMs require PyTorch 2.0 for use with `transformers`! For fast inference with Falcon, check-out [Text Generation Inference](https://github.com/huggingface/text-generation-inference)! Read more in this [blogpost]((https://huggingface.co/blog/falcon). You will need at least 16GB of memory to swiftly run inference with Falcon-7B. # Model Card for Falcon-7B ## Model Details ### Model Description - Developed by: [https://www.tii.ae](https://www.tii.ae); - Model type: Causal decoder-only; - Language(s) (NLP): English and French; - License: Apache 2.0. ### Model Source - Paper: coming soon. ## Uses ### Direct Use Research on large language models; as a foundation for further specialization and finetuning for specific usecases (e.g., summarization, text generation, chatbot, etc.) ### Out-of-Scope Use Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful. ## Bias, Risks, and Limitations Falcon-7B is trained on English and French data only, and will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online. ### Recommendations We recommend users of Falcon-7B to consider finetuning it for the specific set of tasks of interest, and for guardrails and appropriate precautions to be taken for any production use. ## How to Get Started with the Model ```python from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model = "tiiuae/falcon-7b" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, tokenizer=tokenizer, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", ) sequences = pipeline( "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:", max_length=200, do_sample=True, top_k=10, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ``` ## Training Details ### Training Data Falcon-7B was trained on 1,500B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb), a high-quality filtered and deduplicated web dataset which we enhanced with curated corpora. Significant components from our curated copora were inspired by The Pile ([Gao et al., 2020](https://arxiv.org/abs/2101.00027)). \| Data source \| Fraction \| Tokens \| Sources \| \|--------------------\|--------------\|------------\|-----------------------------------\| \| [RefinedWeb-English](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) \| 79% \| 1,185B \| massive web crawl \| \| Books \| 7% \| 110B \| \| \| Conversations \| 6% \| 85B \| Reddit, StackOverflow, HackerNews \| \| Code \| 3% \| 45B \| \| \| RefinedWeb-French \| 3% \| 45B \| massive web crawl \| \| Technical \| 2% \| 30B \| arXiv, PubMed, USPTO, etc. \| The data was tokenized with the Falcon-[7B](https://huggingface.co/tiiuae/falcon-7b)/[40B](https://huggingface.co/tiiuae/falcon-40b) tokenizer. ### Training Procedure Falcon-7B was trained on 384 A100 40GB GPUs, using a 2D parallelism strategy (PP=2, DP=192) combined with ZeRO. #### Training Hyperparameters \| Hyperparameter \| Value \| Comment \| \|--------------------\|------------\|-------------------------------------------\| \| Precision \| `bfloat16` \| \| \| Optimizer \| AdamW \| \| \| Learning rate \| 6e-4 \| 4B tokens warm-up, cosine decay to 1.2e-5 \| \| Weight decay \| 1e-1 \| \| \| Z-loss \| 1e-4 \| \| \| Batch size \| 2304 \| 30B tokens ramp-up \| #### Speeds, Sizes, Times Training happened in early March 2023 and took about two weeks. ## Evaluation Paper coming soon. See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) for early results. ## Technical Specifications ### Model Architecture and Objective Falcon-7B is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token). The architecture is broadly adapted from the GPT-3 paper ([Brown et al., 2020](https://arxiv.org/abs/2005.14165)), with the following differences: * Positionnal embeddings: rotary ([Su et al., 2021](https://arxiv.org/abs/2104.09864)); * Attention: multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)) and FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)); * Decoder-block: parallel attention/MLP with a single layer norm. \| Hyperparameter \| Value \| Comment \| \|--------------------\|-----------\|----------------------------------------\| \| Layers \| 32 \| \| \| `d_model` \| 4544 \| Increased to compensate for multiquery \| \| `head_dim` \| 64 \| Reduced to optimise for FlashAttention \| \| Vocabulary \| 65024 \| \| \| Sequence length \| 2048 \| \| ### Compute Infrastructure #### Hardware Falcon-7B was trained on AWS SageMaker, on 384 A100 40GB GPUs in P4d instances. #### Software Falcon-7B was trained a custom distributed training codebase, Gigatron. It uses a 3D parallelism approach combined with ZeRO and high-performance Triton kernels (FlashAttention, etc.) ## Citation Paper coming soon 😊. In the meanwhile, you can use the following information to cite: ``` @article{falcon40b, title={{Falcon-40B}: an open large language model with state-of-the-art performance}, author={Almazrouei, Ebtesam and Alobeidli, Hamza and Alshamsi, Abdulaziz and Cappelli, Alessandro and Cojocaru, Ruxandra and Debbah, Merouane and Goffinet, Etienne and Heslow, Daniel and Launay, Julien and Malartic, Quentin and Noune, Badreddine and Pannier, Baptiste and Penedo, Guilherme}, year={2023} } ``` To learn more about the pretraining dataset, see the 📓 [RefinedWeb paper](https://arxiv.org/abs/2306.01116). ``` @article{refinedweb, title={The {R}efined{W}eb dataset for {F}alcon {LLM}: outperforming curated corpora with web data, and web data only}, author={Guilherme Penedo and Quentin Malartic and Daniel Hesslow and Ruxandra Cojocaru and Alessandro Cappelli and Hamza Alobeidli and Baptiste Pannier and Ebtesam Almazrouei and Julien Launay}, journal={arXiv preprint arXiv:2306.01116}, eprint={2306.01116}, eprinttype = {arXiv}, url={https://arxiv.org/abs/2306.01116}, year={2023} } ``` ## License Falcon-7B is made available under the Apache 2.0 license. ## Contact [email protected]
TheBloke/GodziLLa-30B-GGML	TheBloke	2023-07-10T09:38:45Z	0	4	null	[ "merge", "mix", "cot", "text-generation", "license:other", "region:us" ]	text-generation	2023-07-09T11:53:15Z	--- inference: false license: other pipeline_tag: text-generation tags: - merge - mix - cot --- <!-- header start --> <div style="width: 100%;"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p><a href="https://discord.gg/theblokeai">Chat & support: my new Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <!-- header end --> # Maya Philippine's GodziLLa 30B GGML These files are GGML format model files for [Maya Philippine's GodziLLa 30B](https://huggingface.co/MayaPH/GodziLLa-30B). GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as: * [text-generation-webui](https://github.com/oobabooga/text-generation-webui) * [KoboldCpp](https://github.com/LostRuins/koboldcpp) * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui) * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) * [ctransformers](https://github.com/marella/ctransformers) ## Licensing This model is GodziLLa-30B, a language model developed by Maya Philippines. Maya Philippines' work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. For more information, visit: https://creativecommons.org/licenses/by-nc/4.0/ This model is based on Meta LLaMA weights, which are licensed under a bespoke research-only non-commercial license. ## Repositories available * [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/GodziLLa-30B-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/GodziLLa-30B-GGML) * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/MayaPH/GodziLLa-30B) ## Prompt template: Alpaca ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: PROMPT ### Response: ``` <!-- compatibility_ggml start --> ## Compatibility ### Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0` These are guaranteed to be compatible with any UIs, tools and libraries released since late May. They may be phased out soon, as they are largely superseded by the new k-quant methods. ### New k-quant methods: `q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K` These new quantisation methods are compatible with llama.cpp as of June 6th, commit `2d43387`. They are now also compatible with recent releases of text-generation-webui, KoboldCpp, llama-cpp-python, ctransformers, rustformers and most others. For compatibility with other tools and libraries, please check their documentation. ## Explanation of the new k-quant methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw * GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type. Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_ggml end --> ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| godzilla-30b.ggmlv3.q2_K.bin \| q2_K \| 2 \| 13.71 GB\| 16.21 GB \| New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. \| \| godzilla-30b.ggmlv3.q3_K_S.bin \| q3_K_S \| 3 \| 14.06 GB\| 16.56 GB \| New k-quant method. Uses GGML_TYPE_Q3_K for all tensors \| \| godzilla-30b.ggmlv3.q3_K_M.bin \| q3_K_M \| 3 \| 15.72 GB\| 18.22 GB \| New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K \| \| godzilla-30b.ggmlv3.q3_K_L.bin \| q3_K_L \| 3 \| 17.28 GB\| 19.78 GB \| New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K \| \| godzilla-30b.ggmlv3.q4_K_S.bin \| q4_K_S \| 4 \| 18.36 GB\| 20.86 GB \| New k-quant method. Uses GGML_TYPE_Q4_K for all tensors \| \| godzilla-30b.ggmlv3.q4_K_M.bin \| q4_K_M \| 4 \| 19.62 GB\| 22.12 GB \| New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K \| \| godzilla-30b.ggmlv3.q4_0.bin \| q4_0 \| 4 \| 18.30 GB\| 20.80 GB \| Original quant method, 4-bit. \| \| godzilla-30b.ggmlv3.q4_1.bin \| q4_1 \| 4 \| 20.33 GB\| 22.83 GB \| Original quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. \| \| godzilla-30b.ggmlv3.q5_K_S.bin \| q5_K_S \| 5 \| 22.40 GB\| 24.90 GB \| New k-quant method. Uses GGML_TYPE_Q5_K for all tensors \| \| godzilla-30b.ggmlv3.q5_K_M.bin \| q5_K_M \| 5 \| 23.05 GB\| 25.55 GB \| New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K \| \| godzilla-30b.ggmlv3.q5_0.bin \| q5_0 \| 5 \| 22.37 GB\| 24.87 GB \| Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. \| \| godzilla-30b.ggmlv3.q5_1.bin \| q5_1 \| 5 \| 24.40 GB\| 26.90 GB \| Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference. \| \| godzilla-30b.ggmlv3.q6_K.bin \| q6_K \| 6 \| 26.69 GB\| 29.19 GB \| New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors \| \| godzilla-30b.ggmlv3.q8_0.bin \| q8_0 \| 8 \| 34.56 GB\| 37.06 GB \| Original quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. ## How to run in `llama.cpp` I use the following command line; adjust for your tastes and needs: ``` ./main -t 10 -ngl 32 -m godzilla-30b.ggmlv3.q4_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: Write a story about llamas\n### Response:" ``` Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`. Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md). <!-- footer start --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute. Thanks to the [chirper.ai](https://chirper.ai) team! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Luke from CarbonQuill, Aemon Algiz. Patreon special mentions: RoA, Lone Striker, Gabriel Puliatti, Derek Yates, Randy H, Jonathan Leane, Eugene Pentland, Karl Bernard, Viktor Bowallius, senxiiz, Daniel P. Andersen, Pierre Kircher, Deep Realms, Cory Kujawski, Oscar Rangel, Fen Risland, Ajan Kanaga, LangChain4j, webtim, Nikolai Manek, Trenton Dambrowitz, Raven Klaugh, Kalila, Khalefa Al-Ahmad, Chris McCloskey, Luke @flexchar, Ai Maven, Dave, Asp the Wyvern, Sean Connelly, Imad Khwaja, Space Cruiser, Rainer Wilmers, subjectnull, Alps Aficionado, Willian Hasse, Fred von Graf, Artur Olbinski, Johann-Peter Hartmann, WelcomeToTheClub, Willem Michiel, Michael Levine, Iucharbius , Spiking Neurons AB, K, biorpg, John Villwock, Pyrater, Greatston Gnanesh, Mano Prime, Junyu Yang, Stephen Murray, John Detwiler, Luke Pendergrass, terasurfer , Pieter, zynix , Edmond Seymore, theTransient, Nathan LeClaire, vamX, Kevin Schuppel, Preetika Verma, ya boyyy, Alex , SuperWojo, Ghost , Joseph William Delisle, Matthew Berman, Talal Aujan, chris gileta, Illia Dulskyi. Thank you to all my generous patrons and donaters! <!-- footer end --> # Original model card: Maya Philippine's GodziLLa 30B <img src="https://drive.google.com/uc?export=view&id=16DzZwhqybQvT1wQVp-6qXHI9HhKft6CR" width="50%" alt="GodziLLa-30B"> Released July 9, 2023 ## Model Description GodziLLa-30B is an experimental combination of various proprietary Maya LoRAs with CalderaAI's [Lazarus-30B](https://huggingface.co/CalderaAI/30B-Lazarus). This composite model is not meant for any other use outside of research on competing LoRA adapter behavior. More specifically, since this is inherently a LlaMA model, commercial use is prohibited. This model's primary purpose is to stress test the limitations of composite LLMs and observe its performance with respect to other LLMs available on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). ![Godzilla Let Them Fight Meme GIF](https://media.tenor.com/AZkmVImwd5YAAAAC/godzilla-let-them-fight.gif) ## Recommended Prompt Format Alpaca's instruction is the recommended prompt format, but Vicuna's instruction format may also work. ## Usage To use GodziLLa-30B, you are required to provide attribution in accordance with the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. Please include the following attribution notice when utilizing GodziLLa-30B in your work: ```python # This code uses GodziLLa-30B, a language model developed by Maya Philippines. # The model is licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. # For more information, visit: https://creativecommons.org/licenses/by-nc/4.0/ from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("MayaPH/GodziLLa-30B") model = AutoModelForCausalLM.from_pretrained("MayaPH/GodziLLa-30B") ``` Please ensure that you include the relevant attribution notice in your code or any other form of usage and restrict your usage to non-commercial use to comply with the license terms. ## Ethical Considerations When using GodziLLa-30B, it is important to consider the following ethical considerations: 1. Privacy and Security: Avoid sharing sensitive personal information while interacting with the model. The model does not have privacy safeguards, so exercise caution when discussing personal or confidential matters. 2. Fairness and Bias: The model's responses may reflect biases present in the training data. Be aware of potential biases and make an effort to evaluate responses critically and fairly. 3. Transparency: The model operates as a predictive text generator based on patterns learned from the training data. The model's inner workings and the specific training data used are proprietary and not publicly available. 4. User Responsibility: Users should take responsibility for their own decisions and not solely rely on the information provided by the model. Consult with the appropriate professionals or reliable sources for specific advice or recommendations. 5. NSFW Content: The model is a merge of multiple model checkpoints and LoRA adapters. It is highly likely that the resulting model contains uncensored content that may include, but is not limited to, violence, gore, explicit language, and sexual content. If you plan to further refine this model for safe/aligned usage, you are highly encouraged to implement guardrails along with it. ## Further Information For additional information or inquiries about GodziLLa-30B, please contact the Maya Philippines iOps Team via [email protected]. ## Disclaimer GodziLLa-30B is an AI language model from Maya Philippines. It is provided "as is" without warranty of any kind, express or implied. The model developers and Maya Philippines shall not be liable for any direct or indirect damages arising from the use of this model. ## Acknowledgments The development of GodziLLa-30B was made possible by Maya Philippines and the curation of the various proprietary datasets and creation of the different proprietary LoRA adapters.
ArimaKana38/alpaca-cmkl	ArimaKana38	2023-07-10T09:33:02Z	2	0	peft	[ "peft", "region:us" ]	null	2023-07-08T11:17:05Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0
Arindam75/a2c-PandaReachDense-v2	Arindam75	2023-07-10T09:27:33Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T09:24:51Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -1.60 +/- 0.59 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v2 This is a trained model of a A2C agent playing PandaReachDense-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 ... ```
nolanaatama/stnmrshsthprkrvcv2300pchrhys	nolanaatama	2023-07-10T09:19:39Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-10T09:16:40Z	--- license: creativeml-openrail-m ---
Salama1429/TTS_German_Speecht5_finetuned_voxpopuli_nl	Salama1429	2023-07-10T09:13:40Z	326	0	transformers	[ "transformers", "pytorch", "tensorboard", "speecht5", "text-to-audio", "generated_from_trainer", "text-to-speech", "nl", "dataset:facebook/voxpopuli", "license:mit", "endpoints_compatible", "region:us" ]	text-to-speech	2023-07-10T06:43:53Z	--- language: - nl license: mit tags: - generated_from_trainer datasets: - facebook/voxpopuli model-index: - name: TTS_German_Speecht5_finetuned_voxpopuli_nl results: [] pipeline_tag: text-to-speech --- <!-- 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. --> # TTS_German_Speecht5_finetuned_voxpopuli_nl This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the facebook/voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.4593 ## 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: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.5248 \| 4.3 \| 1000 \| 0.4792 \| \| 0.5019 \| 8.61 \| 2000 \| 0.4663 \| \| 0.4937 \| 12.91 \| 3000 \| 0.4609 \| \| 0.4896 \| 17.21 \| 4000 \| 0.4593 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
crisU8/bert-finetuned-ner-clinical-plncmm-large-22	crisU8	2023-07-10T09:04:25Z	109	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-07-10T08:59:40Z	--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner-clinical-plncmm-large-22 results: [] --- <!-- 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-clinical-plncmm-large-22 This model is a fine-tuned version of [plncmm/beto-clinical-wl-es](https://huggingface.co/plncmm/beto-clinical-wl-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2380 - Precision: 0.7554 - Recall: 0.8271 - F1: 0.7896 - Accuracy: 0.9320 ## 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: 24 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 400 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.5983 \| 1.0 \| 572 \| 0.2405 \| 0.7044 \| 0.7964 \| 0.7476 \| 0.9227 \| \| 0.1979 \| 2.0 \| 1144 \| 0.2421 \| 0.7296 \| 0.8189 \| 0.7717 \| 0.9275 \| \| 0.1406 \| 3.0 \| 1716 \| 0.2380 \| 0.7554 \| 0.8271 \| 0.7896 \| 0.9320 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
JensTiburski/test	JensTiburski	2023-07-10T09:00:29Z	0	0	null	[ "de", "region:us" ]	null	2023-07-10T08:56:13Z	--- language: - de --- from diffusers import StableDiffusionLDM3DPipeline pipe_ldm3d = StableDiffusionLDM3DPipeline.from_pretrained("Intel/ldm3d") prompt = "A picture of a castle in the mountains" output = pipe_ldm3d(prompt) obj_file = output.obj obj_file[0].save("castle_ldm3d.obj") --- license: cc ---
soBeauty/xlm-roberta-base-09072023-revised_2	soBeauty	2023-07-10T08:59:49Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "fill-mask", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-07-09T15:31:18Z	--- license: mit tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlm-roberta-base-09072023-revised_2 results: [] --- <!-- 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. --> # xlm-roberta-base-09072023-revised_2 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Accuracy: 0.5577 - Loss: 2.2632 ## 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Accuracy \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:--------:\|:---------------:\| \| 2.725 \| 3.85 \| 500 \| 0.4944 \| 2.4300 \| \| 2.5409 \| 7.69 \| 1000 \| 0.5577 \| 2.2632 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
Aeala/Chronoboros-33b-4bit	Aeala	2023-07-10T08:48:19Z	5	0	transformers	[ "transformers", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-07-10T06:52:52Z	4-bit GPTQ quantization of the [chronoboros-33b](https://huggingface.co/Henk717/chronoboros-33B) merge.
danbrown/checkpoints	danbrown	2023-07-10T08:43:17Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-10T08:20:33Z	--- license: creativeml-openrail-m --- This is a collection of Stable Diffusion model checkpoints, just like my other loras collection. I may be listing it with more details here as I add the models. The models here can be third-party checkpoints, or personal experiments.
SpringYung/falcon_with_10latex	SpringYung	2023-07-10T08:42:21Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-10T08:41:51Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.4.0.dev0
SpringYung/dolly_with_10examples	SpringYung	2023-07-10T08:30:27Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-10T08:30:04Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.4.0.dev0
crisU8/bert-finetuned-ner-clinical-plncmm-large-15	crisU8	2023-07-10T08:15:12Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-07-10T07:57:20Z	--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner-clinical-plncmm-large-15 results: [] --- <!-- 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-clinical-plncmm-large-15 This model is a fine-tuned version of [plncmm/beto-clinical-wl-es](https://huggingface.co/plncmm/beto-clinical-wl-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2339 - Precision: 0.7526 - Recall: 0.8282 - F1: 0.7886 - Accuracy: 0.9309 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 400 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 429 \| 0.2466 \| 0.6954 \| 0.7958 \| 0.7423 \| 0.9223 \| \| 0.5736 \| 2.0 \| 858 \| 0.2380 \| 0.7354 \| 0.8178 \| 0.7744 \| 0.9264 \| \| 0.1845 \| 3.0 \| 1287 \| 0.2339 \| 0.7526 \| 0.8282 \| 0.7886 \| 0.9309 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
komo-dono/matsuoka	komo-dono	2023-07-10T08:14:09Z	0	0	null	[ "region:us" ]	null	2023-07-10T08:12:42Z	--- license: openrail language: - ja tags: - music matsuoka 500 epoch
guaguale/model_kthv_vcg	guaguale	2023-07-10T08:11:44Z	0	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-09T18:50:52Z	--- license: creativeml-openrail-m base_model: /mmu_vcg_ssd/liuhao12/workspace/1_diffusion/models/sd-zhuxiongwei-320nodes-task3_2-0612/checkpoint-28000/ instance_prompt: a male idol sks with blonde hair, wearing a black jacket and fringes on the sides of the jacket tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - guaguale/model_kthv_vcg This is a dreambooth model derived from /mmu_vcg_ssd/liuhao12/workspace/1_diffusion/models/sd-zhuxiongwei-320nodes-task3_2-0612/checkpoint-28000/. The weights were trained on a male idol sks with blonde hair, wearing a black jacket and fringes on the sides of the jacket using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.
lloydchang/wongstein-vide-noir	lloydchang	2023-07-10T07:49:17Z	207	1	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "text-generation-inference", "en", "dataset:amazon_us_reviews", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-07-10T07:43:16Z	--- license: creativeml-openrail-m datasets: - amazon_us_reviews language: - en tags: - text-generation-inference ---
crisU8/bert-finetuned-ner-clinical-plncmm-large-13	crisU8	2023-07-10T07:39:09Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-07-10T07:21:14Z	--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner-clinical-plncmm-large-13 results: [] --- <!-- 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-clinical-plncmm-large-13 This model is a fine-tuned version of [plncmm/beto-clinical-wl-es](https://huggingface.co/plncmm/beto-clinical-wl-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2336 - Precision: 0.7488 - Recall: 0.8227 - F1: 0.7840 - Accuracy: 0.9308 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 300 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 429 \| 0.2408 \| 0.7044 \| 0.8123 \| 0.7545 \| 0.9223 \| \| 0.5338 \| 2.0 \| 858 \| 0.2382 \| 0.7322 \| 0.8178 \| 0.7726 \| 0.9264 \| \| 0.1771 \| 3.0 \| 1287 \| 0.2336 \| 0.7488 \| 0.8227 \| 0.7840 \| 0.9308 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
NasimB/gpt2-concat-guten-mod-rm-ref-2k-rarity-2p5k-p13k	NasimB	2023-07-10T07:28:38Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-10T05:33:27Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: gpt2-concat-guten-mod-rm-ref-2k-rarity-2p5k-p13k results: [] --- <!-- 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. --> # gpt2-concat-guten-mod-rm-ref-2k-rarity-2p5k-p13k This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 3.1752 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.7031 \| 0.29 \| 500 \| 5.6491 \| \| 5.3381 \| 0.59 \| 1000 \| 5.2075 \| \| 4.9932 \| 0.88 \| 1500 \| 4.9530 \| \| 4.7151 \| 1.17 \| 2000 \| 4.8058 \| \| 4.5556 \| 1.46 \| 2500 \| 4.6811 \| \| 4.4516 \| 1.76 \| 3000 \| 4.5752 \| \| 4.3291 \| 2.05 \| 3500 \| 4.4930 \| \| 4.1341 \| 2.34 \| 4000 \| 4.4457 \| \| 4.1006 \| 2.63 \| 4500 \| 4.3896 \| \| 4.0621 \| 2.93 \| 5000 \| 4.3371 \| \| 3.8509 \| 3.22 \| 5500 \| 4.3335 \| \| 3.8058 \| 3.51 \| 6000 \| 4.2974 \| \| 3.7835 \| 3.81 \| 6500 \| 4.2701 \| \| 3.6851 \| 4.1 \| 7000 \| 4.2656 \| \| 3.5155 \| 4.39 \| 7500 \| 4.2594 \| \| 3.5136 \| 4.68 \| 8000 \| 4.2428 \| \| 3.5037 \| 4.98 \| 8500 \| 4.2302 \| \| 3.3411 \| 5.27 \| 9000 \| 4.2422 \| \| 3.321 \| 5.56 \| 9500 \| 4.2417 \| \| 3.323 \| 5.85 \| 10000 \| 4.2412 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
chizhikchi/sci-five-radsum23	chizhikchi	2023-07-10T07:27:40Z	209	0	transformers	[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "summarization", "medical", "clinical", "en", "dataset:MIMIC-III", "license:afl-3.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	summarization	2023-05-02T10:22:12Z	--- license: afl-3.0 tags: - summarization - t5 - medical - clinical language: en datasets: - MIMIC-III widget: - again noted is the large intraparenchymal hemorrhage in the posterior right frontal lobe with extension into both lateral ventricles. the degree of surrounding edema and effacement of adjacent sulci is unchanged. there is minor contralateral shift of normal midline structures. the ventricular size is unchanged. subarachnoid blood is now seen in the left frontal and parietal lobes, likely due to recirculation of the ventricular blood. - a least two attempts were made at imaging, however, the study remains severely limited by patient motion. minimal hyperdensity tracks along a left parietal sulcus (2a:18) is equivocal for a small subarachnoid hemorhage. there is no large mass effect detected. there is no shift of normally midline structures. a minimally displaced zygomatic fracture is present (2a:9). the middle ear cavities, mastoid air cells are clear. there is extensive soft tissue swelling overlying the right frontal calvarium with swelling extending to the right preseptal soft tissues (2a:12). there is mild - moderate mucosal thickening within the ethmoid and maxillary sinuses with some fluid and fluid mucosal thickening in the sphenoid sinus. inference: parameters: max_length: 350 metrics: - rouge-l --- # Impression section Generator For Radiology Reports 🏥 This model is is the result of participation of SINAI team in [Task 1B: Radiology Report Summarization](https://vilmedic.app/misc/bionlp23/sharedtask) at the BioNLP workshop held on ACL 2023. The goal of this task is to foster development of automatic radiology report summarization systems and expanding their applicability by incorporating seven different modalities and anatomies in the provided data. We propose to automate the generation of radiology impressions with "sequence-to-sequence" learning that leverages the power of publicly available pre-trained models, both general domain and biomedical domain-specific. This repository provides access to our best-performing system that resulted from fine-tuning of [Sci-Five base](https://huggingface.co/razent/SciFive-base-Pubmed_PMC), which is T5 model trained for extra 200k steps to optimize it in the context of biomedical literature. # Results The official evaluation results prove that adaptation of a general-domain system for biomedical literature is beneficial for the subsequent fine-tuning for radiology report summarization task. The Table below summarizes the official scores obtained by this model during the official evaluation. Team standings re available [here](https://vilmedic.app/misc/bionlp23/leaderboard/). \| BLEU4 \| ROUGE-L \| BERTscore \| F1-RadGraph \|-----------\|--------\|----------\|----------\| \| 017.38 \| 32.32 \| 55.04 \| 33.96 \| # System description paper and citation The paper with the detailed description of the system is published in the [Proceedings of the 22st Workshop on Biomedical Language Processing](https://aclanthology.org/2023.bionlp-1.53/). BibTeX citation: ``` @inproceedings{chizhikova-etal-2023-sinai, title = "{SINAI} at {R}ad{S}um23: Radiology Report Summarization Based on Domain-Specific Sequence-To-Sequence Transformer Model", author = "Chizhikova, Mariia and Diaz-Galiano, Manuel and Urena-Lopez, L. Alfonso and Martin-Valdivia, M. Teresa", booktitle = "The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.bionlp-1.53", pages = "530--534", abstract = "This paper covers participation of the SINAI team in the shared task 1B: Radiology Report Summarization at the BioNLP workshop held on ACL 2023. Our proposal follows a sequence-to-sequence approach which leverages pre-trained multilingual general domain and monolingual biomedical domain pre-trained language models. The best performing system based on domain-specific model reached 33.96 F1RadGraph score which is the fourth best result among the challenge participants. This model was made publicly available on HuggingFace. We also describe an attempt of Proximal Policy Optimization Reinforcement Learning that was made in order to improve the factual correctness measured with F1RadGraph but did not lead to satisfactory results.", } ```
zhundred/Taxi-v3	zhundred	2023-07-10T07:27:01Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T07:26:59Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 2.73 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="zhundred/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
zwtharry/ppo-Huggy	zwtharry	2023-07-10T07:13:03Z	14	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-07-10T07:12:52Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: zwtharry/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
yuuhan/roberta-base-rte-lora-layer6-11	yuuhan	2023-07-10T07:04:43Z	3	0	peft	[ "peft", "region:us" ]	null	2023-07-07T02:32:01Z	--- library_name: peft --- ## Training procedure ### Framework versions RTE acc: 0.7364620938628159 - PEFT 0.4.0.dev0
crisU8/bert-finetuned-ner-clinical-plncmm-large-11	crisU8	2023-07-10T07:00:16Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-07-10T06:38:46Z	--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner-clinical-plncmm-large-11 results: [] --- <!-- 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-clinical-plncmm-large-11 This model is a fine-tuned version of [plncmm/beto-clinical-wl-es](https://huggingface.co/plncmm/beto-clinical-wl-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2334 - Precision: 0.7534 - Recall: 0.8216 - F1: 0.7860 - Accuracy: 0.9328 ## 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: 32 - eval_batch_size: 32 - 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: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 429 \| 0.2492 \| 0.6813 \| 0.7849 \| 0.7294 \| 0.9211 \| \| 0.6251 \| 2.0 \| 858 \| 0.2420 \| 0.7467 \| 0.8189 \| 0.7812 \| 0.9288 \| \| 0.1942 \| 3.0 \| 1287 \| 0.2334 \| 0.7534 \| 0.8216 \| 0.7860 \| 0.9328 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
hoanghoavienvo/bert-large-uncased-detect-depression-stage-one	hoanghoavienvo	2023-07-10T06:55:05Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-09T22:16:38Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-large-uncased-detect-depression-stage-one results: [] --- <!-- 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-large-uncased-detect-depression-stage-one This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 0.7875 - eval_accuracy: 0.752 - eval_f1: 0.8092 - eval_runtime: 112.7187 - eval_samples_per_second: 8.872 - eval_steps_per_second: 2.218 - epoch: 3.0 - step: 4506 ## 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-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
hell1/rare-puppers	hell1	2023-07-10T06:39:55Z	194	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-10T06:39:48Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: rare-puppers results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.939393937587738 --- # rare-puppers Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### corgi ![corgi](images/corgi.jpg) #### samoyed ![samoyed](images/samoyed.jpg) #### shiba inu ![shiba inu](images/shiba_inu.jpg)
charqican/nominal-groups-recognition-bert-base-spanish-wwm-cased	charqican	2023-07-10T06:37:54Z	118	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "es", "dataset:charqican/spanish_nominal_groups_conll2003", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-07-10T06:30:03Z	--- language: - es tags: - generated_from_trainer datasets: - charqican/spanish_nominal_groups_conll2003 model-index: - name: nominal-groups-recognition-bert-base-spanish-wwm-cased results: [] --- <!-- 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. --> # nominal-groups-recognition-bert-base-spanish-wwm-cased This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on the charqican/spanish_nominal_groups_conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.2772 - Ng Precision: 0.7140 - Ng Recall: 0.7695 - Ng F1: 0.7407 - Ng Number: 3198 - Overall Precision: 0.7140 - Overall Recall: 0.7695 - Overall F1: 0.7407 - Overall Accuracy: 0.8993 ## 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: 13 - 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 \| Ng Precision \| Ng Recall \| Ng F1 \| Ng Number \| Overall Precision \| Overall Recall \| Overall F1 \| Overall Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------------:\|:---------:\|:------:\|:---------:\|:-----------------:\|:--------------:\|:----------:\|:----------------:\| \| 0.3988 \| 1.0 \| 228 \| 0.2792 \| 0.7108 \| 0.7577 \| 0.7335 \| 3198 \| 0.7108 \| 0.7577 \| 0.7335 \| 0.8935 \| \| 0.2257 \| 2.0 \| 456 \| 0.2772 \| 0.7140 \| 0.7695 \| 0.7407 \| 3198 \| 0.7140 \| 0.7695 \| 0.7407 \| 0.8993 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
crisU8/bert-finetuned-ner-clinical-plncmm-large-9	crisU8	2023-07-10T06:27:42Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-07-10T06:18:40Z	--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner-clinical-plncmm-large-9 results: [] --- <!-- 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-clinical-plncmm-large-9 This model is a fine-tuned version of [plncmm/beto-clinical-wl-es](https://huggingface.co/plncmm/beto-clinical-wl-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2499 - Precision: 0.7544 - Recall: 0.8244 - F1: 0.7878 - Accuracy: 0.9331 ## 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: 32 - eval_batch_size: 32 - 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: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 429 \| 0.2543 \| 0.6951 \| 0.7794 \| 0.7348 \| 0.9190 \| \| 0.641 \| 2.0 \| 858 \| 0.2443 \| 0.7410 \| 0.8117 \| 0.7748 \| 0.9273 \| \| 0.1943 \| 3.0 \| 1287 \| 0.2388 \| 0.7378 \| 0.8156 \| 0.7748 \| 0.9304 \| \| 0.1211 \| 4.0 \| 1716 \| 0.2499 \| 0.7544 \| 0.8244 \| 0.7878 \| 0.9331 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
Sukmin/ppo-SnowballTarget	Sukmin	2023-07-10T06:25:07Z	4	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-07-10T06:22:30Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Sukmin/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
jhan405/sd-class-butterflies-64	jhan405	2023-07-10T06:22:38Z	30	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2023-07-10T06:21:51Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('jhan405/sd-class-butterflies-64') image = pipeline().images[0] image ```
crisU8/bert-finetuned-ner-clinical-plncmm-large-7	crisU8	2023-07-10T06:09:28Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-07-10T06:00:18Z	--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner-clinical-plncmm-large-7 results: [] --- <!-- 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-clinical-plncmm-large-7 This model is a fine-tuned version of [plncmm/beto-clinical-wl-es](https://huggingface.co/plncmm/beto-clinical-wl-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2317 - Precision: 0.7503 - Recall: 0.8227 - F1: 0.7848 - Accuracy: 0.9326 ## 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: 32 - eval_batch_size: 32 - 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: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 429 \| 0.2497 \| 0.6860 \| 0.7794 \| 0.7297 \| 0.9201 \| \| 0.6187 \| 2.0 \| 858 \| 0.2391 \| 0.7384 \| 0.8134 \| 0.7741 \| 0.9293 \| \| 0.1936 \| 3.0 \| 1287 \| 0.2317 \| 0.7503 \| 0.8227 \| 0.7848 \| 0.9326 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
digiplay/TEXT	digiplay	2023-07-10T06:07:16Z	0	0	null	[ "license:other", "region:us" ]	null	2023-07-10T06:00:56Z	--- license: other --- TEXTUAL INVERSION Speech Bubble https://civitai.com/models/103237/speech-bubble Trigger Words: *SpeechBubble-Test* ![Screenshot_20230710_140542_Vivaldi Browser Snapshot.jpg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/pdJIqCOyXh1crJ2jIW2jk.jpeg)
edgamer/Taxi-v3	edgamer	2023-07-10T05:50:16Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T05:34:15Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="edgamer/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
Sukmin/Reinforce-PixelCopter	Sukmin	2023-07-10T05:46:30Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T03:34:22Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-PixelCopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 35.40 +/- 26.24 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
jhan405/sd-class-butterflies-32	jhan405	2023-07-10T05:41:33Z	30	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2023-07-10T05:40:27Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('jhan405/sd-class-butterflies-32') image = pipeline().images[0] image ```
desplesda/marian-finetuned-kde4-en-to-fr	desplesda	2023-07-10T05:39:43Z	102	0	transformers	[ "transformers", "pytorch", "marian", "text2text-generation", "translation", "generated_from_trainer", "dataset:kde4", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-09T14:25:59Z	--- license: apache-2.0 tags: - translation - generated_from_trainer datasets: - kde4 metrics: - bleu model-index: - name: marian-finetuned-kde4-en-to-fr results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: kde4 type: kde4 config: en-fr split: train args: en-fr metrics: - name: Bleu type: bleu value: 52.86847876856917 --- <!-- 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. --> # marian-finetuned-kde4-en-to-fr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset. It achieves the following results on the evaluation set: - Loss: 0.8559 - Bleu: 52.8685 ## 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: 64 - 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 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
edgamer/q-FrozenLake-v1-4x4-noSlippery	edgamer	2023-07-10T05:29:25Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T05:29:22Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="edgamer/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
georgeleung30/bert-finetuned-n2c2-ner	georgeleung30	2023-07-10T05:26:17Z	103	1	transformers	[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-07-10T05:19:27Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-n2c2-ner results: [] --- <!-- 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-n2c2-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2116 - Precision: 0.9059 - Recall: 0.8858 - F1: 0.8958 - Accuracy: 0.9758 ## 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: 4 - 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0545 \| 1.0 \| 10469 \| 0.1255 \| 0.8929 \| 0.8933 \| 0.8931 \| 0.9762 \| \| 0.0639 \| 2.0 \| 20938 \| 0.1136 \| 0.8933 \| 0.8784 \| 0.8858 \| 0.9747 \| \| 0.0452 \| 3.0 \| 31407 \| 0.1221 \| 0.8864 \| 0.8991 \| 0.8927 \| 0.9753 \| \| 0.0284 \| 4.0 \| 41876 \| 0.1453 \| 0.9003 \| 0.8821 \| 0.8911 \| 0.9756 \| \| 0.0269 \| 5.0 \| 52345 \| 0.1587 \| 0.9011 \| 0.8934 \| 0.8972 \| 0.9765 \| \| 0.0202 \| 6.0 \| 62814 \| 0.1756 \| 0.9190 \| 0.8660 \| 0.8917 \| 0.9755 \| \| 0.0153 \| 7.0 \| 73283 \| 0.1818 \| 0.9063 \| 0.8831 \| 0.8945 \| 0.9757 \| \| 0.0119 \| 8.0 \| 83752 \| 0.2012 \| 0.9163 \| 0.8744 \| 0.8948 \| 0.9760 \| \| 0.0122 \| 9.0 \| 94221 \| 0.1986 \| 0.9001 \| 0.8908 \| 0.8954 \| 0.9757 \| \| 0.0073 \| 10.0 \| 104690 \| 0.2116 \| 0.9059 \| 0.8858 \| 0.8958 \| 0.9758 \| ### Framework versions - Transformers 4.30.2 - Pytorch 1.8.1+cu111 - Datasets 2.13.1 - Tokenizers 0.13.3
Martinez66/Helluva_Boss_FastyDubs_AllStars	Martinez66	2023-07-10T05:14:24Z	0	0	null	[ "license:openrail", "region:us" ]	null	2023-07-10T04:06:48Z	--- license: openrail --- This is a Spanish version Here you will find the majority of the voices that have participated in Helluva Boss in Spanish, in the Fasty Dubs channel below its configuration and algorithms are shown: Epochs Type Name Voice 180 harvest Agente-1-FalloSinCovers @FalloSinCovers 175 harvest Agente-2-PatriciaAzan @PatriciaAzan 240 harvest Andrealphus-RodoBalderasLocutor @RodoBalderasLocutor 800 harvest Blitzo-Fasty-Dubs @FastyDubs 205 harvest Chazwick-Thurman-RyusakiDub @RyusakiDub 160 harvest Cletus-GeovarockFandubs @GeovarockFandubs 200 harvest Collin-Blowzyblue @blowzyblue 210 mangio-crepe 60 Crimson-Maximtru5314 @maximtru5314 250 mangio-crepe 64 Deerie-KarlyPerCastle @KarlyPerCastle 200 mangio-crepe 164 Fizzarolli-Pafoparasito @pafoparasito 300 magio-crepe 64 Keenie-UnBacconFrito @UnBacconFrito 550 harvest Loona-Hakusagiart @hakusagiart 240 magio-crepe 64 Loopty-Goopty-Pafoparasito @pafoparasito 350 crepe Lyle-Lipton-Guonejo @Guonejo 750 harvest Millie-Karu @karucovers_gt 780 harvest Moxxie-Rubecai @Rubecai 650 harvest Ozzie-Asmodeo-Cancion-Maximtru5314 @SantiagoVoiceo Dialogos-SantiagoVoiceo @maximtru5314 420 harvest Octavia-Mary-Ciel @Jenncathvoice 200 magio-crepe 40 Policia-Guonejo @Guonejo 450 harvest Stella-Sacrefleur https://www.instagram.com/sacrefleur/ 750 harvest Stolas-LaWeaAstral @LaWeaAstral 400 harvest Striker-Azzydubs5621 @azzydubs5621 600 magio-crepe 64 Verosika-Mayday-KarlyPerCastle @KarlyPerCastle 300 harvest Vortex-RodoBalderas @RodoBalderasLocutor 200 harvest Wally-Wackford-RyusakiDub @RyusakiDub Important: If you are going to use these voices make sure you give credit to the people who brought it to life. Give credits to the credro of the voices ::::::::::::::::::::::::::::::::::::::::::: Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for fair use for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.
crisU8/bert-finetuned-ner-clinical-plncmm-large-4	crisU8	2023-07-10T05:12:57Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-07-10T05:01:35Z	--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner-clinical-plncmm-large-4 results: [] --- <!-- 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-clinical-plncmm-large-4 This model is a fine-tuned version of [plncmm/beto-clinical-wl-es](https://huggingface.co/plncmm/beto-clinical-wl-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2677 - Precision: 0.7664 - Recall: 0.8337 - F1: 0.7986 - Accuracy: 0.9369 ## 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: 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_steps: 500 - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.6626 \| 1.0 \| 857 \| 0.2329 \| 0.7139 \| 0.8068 \| 0.7575 \| 0.9251 \| \| 0.1834 \| 2.0 \| 1714 \| 0.2479 \| 0.7246 \| 0.8216 \| 0.7701 \| 0.9268 \| \| 0.1162 \| 3.0 \| 2571 \| 0.2504 \| 0.7616 \| 0.8310 \| 0.7948 \| 0.9336 \| \| 0.0862 \| 4.0 \| 3428 \| 0.2677 \| 0.7664 \| 0.8337 \| 0.7986 \| 0.9369 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
smangrul/peft-lora-codegen-25-guanaco-v100-colab	smangrul	2023-07-10T05:11:58Z	9	4	peft	[ "peft", "tensorboard", "generated_from_trainer", "base_model:Salesforce/codegen25-7b-multi_P", "base_model:adapter:Salesforce/codegen25-7b-multi_P", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2023-07-08T10:24:14Z	--- license: apache-2.0 base_model: Salesforce/codegen25-7b-multi tags: - generated_from_trainer model-index: - name: peft-lora-codgen-25-guanaco-t4-colab results: [] library_name: 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. --> # peft-lora-codgen-25-guanaco-t4-colab This model is a fine-tuned version of [Salesforce/codegen25-7b-multi](https://huggingface.co/Salesforce/codegen25-7b-multi) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - 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 0.4.0.dev0 - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
Minggu/anizhchie2	Minggu	2023-07-10T04:51:49Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-10T04:48:59Z	--- license: creativeml-openrail-m ---
hegelty/KcBERT-Large-finetuned-josa-2	hegelty	2023-07-10T04:45:34Z	80	0	transformers	[ "transformers", "tf", "bert", "fill-mask", "generated_from_keras_callback", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-07-10T02:30:56Z	--- tags: - generated_from_keras_callback model-index: - name: hegelty/KcBERT-Large-finetuned-josa-2 results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # hegelty/KcBERT-Large-finetuned-josa-2 This model is a fine-tuned version of [beomi/KcBERT-Large](https://huggingface.co/beomi/KcBERT-Large) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0039 - Validation Loss: 0.0000 - Epoch: 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 81390, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 0.0039 \| 0.0000 \| 0 \| ### Framework versions - Transformers 4.30.2 - TensorFlow 2.9.2 - Datasets 2.13.1 - Tokenizers 0.13.3
crisU8/bert-finetuned-ner-clinical-plncmm-large-1	crisU8	2023-07-10T04:42:21Z	124	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-07-10T04:30:54Z	--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner-clinical-plncmm-large-1 results: [] --- <!-- 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-clinical-plncmm-large-1 This model is a fine-tuned version of [plncmm/beto-clinical-wl-es](https://huggingface.co/plncmm/beto-clinical-wl-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2406 - Precision: 0.7503 - Recall: 0.8227 - F1: 0.7848 - Accuracy: 0.9318 ## 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: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.3906 \| 1.0 \| 857 \| 0.2271 \| 0.7130 \| 0.8101 \| 0.7585 \| 0.9253 \| \| 0.1758 \| 2.0 \| 1714 \| 0.2378 \| 0.7460 \| 0.8222 \| 0.7822 \| 0.9290 \| \| 0.125 \| 3.0 \| 2571 \| 0.2406 \| 0.7503 \| 0.8227 \| 0.7848 \| 0.9318 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
Raj-Sanjay-Shah/babyLM_roberta_base_epoch_5	Raj-Sanjay-Shah	2023-07-10T04:40:28Z	105	0	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-07-10T04:15:35Z	--- license: cc-by-nc-sa-4.0 ---
gameofdimension/lora-cs324-length-control	gameofdimension	2023-07-10T04:38:56Z	0	0	null	[ "tensorboard", "generated_from_trainer", "license:mit", "region:us" ]	null	2023-06-28T15:27:59Z	--- license: mit tags: - generated_from_trainer model-index: - name: lora-cs324-length-control results: [] --- <!-- 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. --> # lora-cs324-length-control This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2118 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.5584 \| 0.16 \| 500 \| 3.2567 \| \| 3.392 \| 0.32 \| 1000 \| 3.2473 \| \| 3.3584 \| 0.48 \| 1500 \| 3.2290 \| \| 3.3115 \| 0.64 \| 2000 \| 3.2196 \| \| 3.3041 \| 0.8 \| 2500 \| 3.2126 \| \| 3.3024 \| 0.96 \| 3000 \| 3.2118 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
bastianchinchon/nominal-groups-recognition-bert-base-spanish-wwm-cased	bastianchinchon	2023-07-10T04:34:17Z	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "es", "dataset:bastianchinchon/spanish_nominal_groups_conll2003", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-07-10T04:06:46Z	--- language: - es tags: - generated_from_trainer datasets: - bastianchinchon/spanish_nominal_groups_conll2003 model-index: - name: nominal-groups-recognition-bert-base-spanish-wwm-cased results: [] --- <!-- 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. --> # nominal-groups-recognition-bert-base-spanish-wwm-cased This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on the bastianchinchon/spanish_nominal_groups_conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.2836 - Body Part Precision: 0.675 - Body Part Recall: 0.7191 - Body Part F1: 0.6964 - Body Part Number: 413 - Disease Precision: 0.7177 - Disease Recall: 0.7405 - Disease F1: 0.7289 - Disease Number: 975 - Family Member Precision: 0.8276 - Family Member Recall: 0.8 - Family Member F1: 0.8136 - Family Member Number: 30 - Medication Precision: 0.8228 - Medication Recall: 0.6989 - Medication F1: 0.7558 - Medication Number: 93 - Procedure Precision: 0.5586 - Procedure Recall: 0.5820 - Procedure F1: 0.5701 - Procedure Number: 311 - Overall Precision: 0.6864 - Overall Recall: 0.7075 - Overall F1: 0.6968 - Overall Accuracy: 0.9146 ## 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: 13 - 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 \| Body Part Precision \| Body Part Recall \| Body Part F1 \| Body Part Number \| Disease Precision \| Disease Recall \| Disease F1 \| Disease Number \| Family Member Precision \| Family Member Recall \| Family Member F1 \| Family Member Number \| Medication Precision \| Medication Recall \| Medication F1 \| Medication Number \| Procedure Precision \| Procedure Recall \| Procedure F1 \| Procedure Number \| Overall Precision \| Overall Recall \| Overall F1 \| Overall Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------------------:\|:----------------:\|:------------:\|:----------------:\|:-----------------:\|:--------------:\|:----------:\|:--------------:\|:-----------------------:\|:--------------------:\|:----------------:\|:--------------------:\|:--------------------:\|:-----------------:\|:-------------:\|:-----------------:\|:-------------------:\|:----------------:\|:------------:\|:----------------:\|:-----------------:\|:--------------:\|:----------:\|:----------------:\| \| 0.4304 \| 1.0 \| 1004 \| 0.2985 \| 0.5620 \| 0.7022 \| 0.6243 \| 413 \| 0.7059 \| 0.6944 \| 0.7001 \| 975 \| 0.8276 \| 0.8 \| 0.8136 \| 30 \| 0.6848 \| 0.6774 \| 0.6811 \| 93 \| 0.5390 \| 0.5113 \| 0.5248 \| 311 \| 0.6415 \| 0.6658 \| 0.6534 \| 0.9028 \| \| 0.2346 \| 2.0 \| 2008 \| 0.2836 \| 0.675 \| 0.7191 \| 0.6964 \| 413 \| 0.7177 \| 0.7405 \| 0.7289 \| 975 \| 0.8276 \| 0.8 \| 0.8136 \| 30 \| 0.8228 \| 0.6989 \| 0.7558 \| 93 \| 0.5586 \| 0.5820 \| 0.5701 \| 311 \| 0.6864 \| 0.7075 \| 0.6968 \| 0.9146 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
aammari/setfit-zero-shot-classification-pbsp-p3-sev	aammari	2023-07-10T04:31:17Z	4	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]	text-classification	2023-07-10T04:28:56Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # setfit-zero-shot-classification-pbsp-p3-sev This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("setfit-zero-shot-classification-pbsp-p3-sev") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```
ghwangbo/Korean_Finetuned_Falcon	ghwangbo	2023-07-10T04:27:34Z	2	1	peft	[ "peft", "region:us" ]	null	2023-07-06T08:32:52Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0.dev0
cwiz/llama-7b-saiga-merged	cwiz	2023-07-10T04:26:24Z	5	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-09T18:56:19Z	--- license: apache-2.0 --- [Saiga](https://huggingface.co/IlyaGusev/saiga_7b_lora) merged with [LLaMa-7b](https://huggingface.co/decapoda-research/llama-7b-hf) for further finetuning.
Raj-Sanjay-Shah/babyLM_roberta_base_epoch_15	Raj-Sanjay-Shah	2023-07-10T03:36:56Z	124	0	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "license:cc-by-nc-nd-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-07-10T03:07:35Z	--- license: cc-by-nc-nd-4.0 ---
eugene-yang/colbertx-xlmr-large-tt-eng.zho	eugene-yang	2023-07-10T03:29:40Z	32	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "en", "zh", "arxiv:2201.08471", "license:mit", "endpoints_compatible", "region:us" ]	null	2023-07-10T03:01:12Z	--- license: mit language: - en - zh task_categories: - text-retrieval - zero-shot-retrieval - information-retrieval - zero-shot-information-retrieval task_ids: - passage-retrieval - cross-language-retrieval --- Model trained by [Suraj Nair](https://srnair.netlify.app/). If you use the model, please cite our paper. ```bibtex @inproceedings{colbert-x, author = {Suraj Nair and Eugene Yang and Dawn Lawrie and Kevin Duh and Paul McNamee and Kenton Murray and James Mayfield and Douglas W. Oard}, title = {Transfer Learning Approaches for Building Cross-Language Dense Retrieval Models}, booktitle = {Proceedings of the 44th European Conference on Information Retrieval (ECIR)}, year = {2022}, url = {https://arxiv.org/abs/2201.08471} } ```
casque/3DMM_V12	casque	2023-07-10T03:29:02Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-10T03:28:24Z	--- license: creativeml-openrail-m ---
casque/Colored_Icons_by_vizsumit	casque	2023-07-10T03:24:54Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-10T03:23:35Z	--- license: creativeml-openrail-m ---
casque/logo_v1-000012	casque	2023-07-10T03:21:55Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-10T03:20:36Z	--- license: creativeml-openrail-m ---
japoople/test	japoople	2023-07-10T03:13:18Z	0	0	nemo	[ "nemo", "text-classification", "en", "dataset:fka/awesome-chatgpt-prompts", "license:openrail", "region:us" ]	text-classification	2023-07-10T03:07:08Z	--- license: openrail datasets: - fka/awesome-chatgpt-prompts language: - en pipeline_tag: text-classification library_name: nemo ---
VFiona/opus-mt-en-it-finetuned_4600-en-to-it	VFiona	2023-07-10T03:06:40Z	105	0	transformers	[ "transformers", "pytorch", "marian", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-07-09T19:45:48Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: opus-mt-en-it-finetuned_4600-en-to-it results: [] --- <!-- 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. --> # opus-mt-en-it-finetuned_4600-en-to-it This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-it](https://huggingface.co/Helsinki-NLP/opus-mt-en-it) 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: 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: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\| \| No log \| 1.0 \| 259 \| 0.5103 \| 66.2359 \| 28.3391 \| ### Framework versions - Transformers 4.30.2 - Pytorch 1.12.1 - Datasets 2.13.1 - Tokenizers 0.11.0
edgamer/ppo-LunarLander-v2	edgamer	2023-07-10T02:51:29Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T01:11:26Z	--- 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: 281.47 +/- 22.46 name: mean_reward verified: false --- # 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 ... ```
NasimB/gpt2-concat-mod-datasets-rarity1-rerun	NasimB	2023-07-10T02:49:37Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-10T00:33:42Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: gpt2-concat-mod-datasets-rarity1-rerun results: [] --- <!-- 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. --> # gpt2-concat-mod-datasets-rarity1-rerun This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 3.0263 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 7 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.7311 \| 0.3 \| 500 \| 5.6497 \| \| 5.3805 \| 0.59 \| 1000 \| 5.2065 \| \| 5.0306 \| 0.89 \| 1500 \| 4.9574 \| \| 4.7526 \| 1.18 \| 2000 \| 4.8142 \| \| 4.6058 \| 1.48 \| 2500 \| 4.6885 \| \| 4.4982 \| 1.78 \| 3000 \| 4.5904 \| \| 4.3593 \| 2.07 \| 3500 \| 4.5261 \| \| 4.185 \| 2.37 \| 4000 \| 4.4783 \| \| 4.154 \| 2.66 \| 4500 \| 4.4233 \| \| 4.1262 \| 2.96 \| 5000 \| 4.3708 \| \| 3.8986 \| 3.26 \| 5500 \| 4.3804 \| \| 3.8767 \| 3.55 \| 6000 \| 4.3494 \| \| 3.8605 \| 3.85 \| 6500 \| 4.3124 \| \| 3.7194 \| 4.14 \| 7000 \| 4.3395 \| \| 3.5981 \| 4.44 \| 7500 \| 4.3194 \| \| 3.5952 \| 4.74 \| 8000 \| 4.3059 \| \| 3.5511 \| 5.03 \| 8500 \| 4.3089 \| \| 3.3393 \| 5.33 \| 9000 \| 4.3236 \| \| 3.3388 \| 5.62 \| 9500 \| 4.3220 \| \| 3.3443 \| 5.92 \| 10000 \| 4.3139 \| \| 3.2213 \| 6.22 \| 10500 \| 4.3304 \| \| 3.1851 \| 6.51 \| 11000 \| 4.3313 \| \| 3.1911 \| 6.81 \| 11500 \| 4.3317 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
nomsgadded/textual_inversion_van_gogh	nomsgadded	2023-07-10T02:47:57Z	48	0	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "base_model:CompVis/stable-diffusion-v1-4", "base_model:adapter:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-10T01:24:49Z	--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - nomsgadded/textual_inversion_van_gogh These are textual inversion adaption weights for CompVis/stable-diffusion-v1-4. You can find some example images in the following.
crisU8/bert-finetuned-ner-clinical-plncmm-8	crisU8	2023-07-10T02:41:04Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-07-10T02:31:37Z	--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner-clinical-plncmm-8 results: [] --- <!-- 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-clinical-plncmm-8 This model is a fine-tuned version of [plncmm/beto-clinical-wl-es](https://huggingface.co/plncmm/beto-clinical-wl-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2568 - Precision: 0.7476 - Recall: 0.8063 - F1: 0.7758 - Accuracy: 0.9277 ## 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: 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_steps: 500 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.6628 \| 1.0 \| 502 \| 0.2590 \| 0.6791 \| 0.7711 \| 0.7222 \| 0.9103 \| \| 0.2168 \| 2.0 \| 1004 \| 0.2309 \| 0.7243 \| 0.7975 \| 0.7591 \| 0.9238 \| \| 0.1301 \| 3.0 \| 1506 \| 0.2568 \| 0.7476 \| 0.8063 \| 0.7758 \| 0.9277 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
harrycools/q-FrozenLake-v1-4x4-noSlippery	harrycools	2023-07-10T02:40:33Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T02:40:31Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="harrycools/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
eatrero/distilbert-base-uncased-finetuned-emotion	eatrero	2023-07-10T02:22:31Z	108	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-09T19:15:02Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9235 - name: F1 type: f1 value: 0.9234507249341903 --- <!-- 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. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2222 - Accuracy: 0.9235 - F1: 0.9235 ## 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: 64 - eval_batch_size: 64 - 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 \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 250 \| 0.3302 \| 0.9005 \| 0.8959 \| \| No log \| 2.0 \| 500 \| 0.2222 \| 0.9235 \| 0.9235 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
douy/T5-11B-Ctrl-Simplification	douy	2023-07-10T02:22:04Z	24	1	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "en", "arxiv:2212.09739", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-07-10T01:44:27Z	--- license: apache-2.0 language: - en --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [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: [https://arxiv.org/abs/2212.09739](https://arxiv.org/abs/2212.09739) - Demo: [http://lens-score.com](http://lens-score.com) ## 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] ## 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 Data 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 Data Card if possible. --> [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 ## 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] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Chickenfish/Monica_stable_v1	Chickenfish	2023-07-10T02:19:37Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-10T02:17:36Z	--- license: creativeml-openrail-m ---
JBJoyce/ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan	JBJoyce	2023-07-10T01:51:39Z	161	0	transformers	[ "transformers", "pytorch", "tensorboard", "audio-spectrogram-transformer", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "license:bsd-3-clause", "endpoints_compatible", "region:us" ]	audio-classification	2023-07-10T00:20:46Z	--- license: bsd-3-clause tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan results: [] --- <!-- 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. --> # ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.4626 - Accuracy: 0.9 ## 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: 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6832 \| 1.0 \| 113 \| 0.6136 \| 0.79 \| \| 0.3528 \| 2.0 \| 226 \| 0.6350 \| 0.77 \| \| 0.178 \| 3.0 \| 339 \| 0.7414 \| 0.8 \| \| 0.142 \| 4.0 \| 452 \| 0.5234 \| 0.84 \| \| 0.1209 \| 5.0 \| 565 \| 0.5176 \| 0.88 \| \| 0.0004 \| 6.0 \| 678 \| 0.4160 \| 0.88 \| \| 0.0002 \| 7.0 \| 791 \| 0.4798 \| 0.9 \| \| 0.0002 \| 8.0 \| 904 \| 0.4693 \| 0.89 \| \| 0.1201 \| 9.0 \| 1017 \| 0.4636 \| 0.9 \| \| 0.0002 \| 10.0 \| 1130 \| 0.4626 \| 0.9 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
EleutherAI/pythia-2.8b-v0	EleutherAI	2023-07-10T01:35:41Z	1,819	5	transformers	[ "transformers", "pytorch", "safetensors", "gpt_neox", "text-generation", "causal-lm", "pythia", "pythia_v0", "en", "dataset:the_pile", "arxiv:2101.00027", "arxiv:2201.07311", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-11-20T03:56:10Z	--- language: - en tags: - pytorch - causal-lm - pythia - pythia_v0 license: apache-2.0 datasets: - the_pile --- The Pythia Scaling Suite is a collection of models developed to facilitate interpretability research. It contains two sets of eight models of sizes 70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two models: one trained on the Pile, and one trained on the Pile after the dataset has been globally deduplicated. All 8 model sizes are trained on the exact same data, in the exact same order. All Pythia models are available [on Hugging Face](https://huggingface.co/models?other=pythia). The Pythia model suite was deliberately designed to promote scientific research on large language models, especially interpretability research. Despite not centering downstream performance as a design goal, we find the models <a href="#evaluations">match or exceed</a> the performance of similar and same-sized models, such as those in the OPT and GPT-Neo suites. Please note that all models in the Pythia suite were renamed in January 2023. For clarity, a <a href="#naming-convention-and-parameter-count">table comparing the old and new names</a> is provided in this model card, together with exact parameter counts. ## Pythia-2.8B ### Model Details - Developed by: [EleutherAI](http://eleuther.ai) - Model type: Transformer-based Language Model - Language: English - Learn more: [Pythia's GitHub repository](https://github.com/EleutherAI/pythia) for training procedure, config files, and details on how to use. - Library: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) - License: Apache 2.0 - Contact: to ask questions about this model, join the [EleutherAI Discord](https://discord.gg/zBGx3azzUn), and post them in `#release-discussion`. Please read the existing Pythia documentation before asking about it in the EleutherAI Discord. For general correspondence: [contact@eleuther. ai](mailto:[email protected]). <figure> \| Pythia model \| Non-Embedding Params \| Layers \| Model Dim \| Heads \| Batch Size \| Learning Rate \| Equivalent Models \| \| -----------: \| -------------------: \| :----: \| :-------: \| :---: \| :--------: \| :-------------------: \| :--------------------: \| \| 70M \| 18,915,328 \| 6 \| 512 \| 8 \| 2M \| 1.0 x 10<sup>-3</sup> \| — \| \| 160M \| 85,056,000 \| 12 \| 768 \| 12 \| 4M \| 6.0 x 10<sup>-4</sup> \| GPT-Neo 125M, OPT-125M \| \| 410M \| 302,311,424 \| 24 \| 1024 \| 16 \| 4M \| 3.0 x 10<sup>-4</sup> \| OPT-350M \| \| 1.0B \| 805,736,448 \| 16 \| 2048 \| 8 \| 2M \| 3.0 x 10<sup>-4</sup> \| — \| \| 1.4B \| 1,208,602,624 \| 24 \| 2048 \| 16 \| 4M \| 2.0 x 10<sup>-4</sup> \| GPT-Neo 1.3B, OPT-1.3B \| \| 2.8B \| 2,517,652,480 \| 32 \| 2560 \| 32 \| 2M \| 1.6 x 10<sup>-4</sup> \| GPT-Neo 2.7B, OPT-2.7B \| \| 6.9B \| 6,444,163,072 \| 32 \| 4096 \| 32 \| 2M \| 1.2 x 10<sup>-4</sup> \| OPT-6.7B \| \| 12B \| 11,327,027,200 \| 36 \| 5120 \| 40 \| 2M \| 1.2 x 10<sup>-4</sup> \| — \| <figcaption>Engineering details for the <i>Pythia Suite</i>. Deduped and non-deduped models of a given size have the same hyperparameters. “Equivalent” models have <b>exactly</b> the same architecture, and the same number of non-embedding parameters.</figcaption> </figure> ### Uses and Limitations #### Intended Use The primary intended use of Pythia is research on the behavior, functionality, and limitations of large language models. This suite is intended to provide a controlled setting for performing scientific experiments. To enable the study of how language models change over the course of training, we provide 143 evenly spaced intermediate checkpoints per model. These checkpoints are hosted on Hugging Face as branches. Note that branch `143000` corresponds exactly to the model checkpoint on the `main` branch of each model. You may also further fine-tune and adapt Pythia-2.8B for deployment, as long as your use is in accordance with the Apache 2.0 license. Pythia models work with the Hugging Face [Transformers Library](https://huggingface.co/docs/transformers/index). If you decide to use pre-trained Pythia-2.8B as a basis for your fine-tuned model, please conduct your own risk and bias assessment. #### Out-of-scope use The Pythia Suite is not intended for deployment. It is not a in itself a product and cannot be used for human-facing interactions. Pythia models are English-language only, and are not suitable for translation or generating text in other languages. Pythia-2.8B has not been fine-tuned for downstream contexts in which language models are commonly deployed, such as writing genre prose, or commercial chatbots. This means Pythia-2.8B will not respond to a given prompt the way a product like ChatGPT does. This is because, unlike this model, ChatGPT was fine-tuned using methods such as Reinforcement Learning from Human Feedback (RLHF) to better “understand” human instructions. #### Limitations and biases The core functionality of a large language model is to take a string of text and predict the next token. The token deemed statistically most likely by the model need not produce the most “accurate” text. Never rely on Pythia-2.8B to produce factually accurate output. This model was trained on [the Pile](https://pile.eleuther.ai/), a dataset known to contain profanity and texts that are lewd or otherwise offensive. See [Section 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a discussion of documented biases with regards to gender, religion, and race. Pythia-2.8B may produce socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. If you plan on using text generated through, for example, the Hosted Inference API, we recommend having a human curate the outputs of this language model before presenting it to other people. Please inform your audience that the text was generated by Pythia-2.8B. ### Quickstart Pythia models can be loaded and used via the following code, demonstrated here for the third `pythia-70m-deduped` checkpoint: ```python from transformers import GPTNeoXForCausalLM, AutoTokenizer model = GPTNeoXForCausalLM.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) tokenizer = AutoTokenizer.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) inputs = tokenizer("Hello, I am", return_tensors="pt") tokens = model.generate(inputs) tokenizer.decode(tokens[0]) ``` Revision/branch `step143000` corresponds exactly to the model checkpoint on the `main` branch of each model.<br> For more information on how to use all Pythia models, see [documentation on GitHub](https://github.com/EleutherAI/pythia). ### Training #### Training data [The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in English. It was created by EleutherAI specifically for training large language models. It contains texts from 22 diverse sources, roughly broken down into five categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl), prose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and miscellaneous (e.g. GitHub, Enron Emails). See [the Pile paper](https://arxiv.org/abs/2101.00027) for a breakdown of all data sources, methodology, and a discussion of ethical implications. Consult [the datasheet](https://arxiv.org/abs/2201.07311) for more detailed documentation about the Pile and its component datasets. The Pile can be downloaded from the [official website](https://pile.eleuther.ai/), or from a [community mirror](https://the-eye.eu/public/AI/pile/).<br> The Pile was not** deduplicated before being used to train Pythia-2.8B. #### Training procedure All models were trained on the exact same data, in the exact same order. Each model saw 299,892,736,000 tokens during training, and 143 checkpoints for each model are saved every 2,097,152,000 tokens, spaced evenly throughout training. This corresponds to training for just under 1 epoch on the Pile for non-deduplicated models, and about 1.5 epochs on the deduplicated Pile. All Pythia models trained for the equivalent of 143000 steps at a batch size of 2,097,152 tokens. Two batch sizes were used: 2M and 4M. Models with a batch size of 4M tokens listed were originally trained for 71500 steps instead, with checkpoints every 500 steps. The checkpoints on Hugging Face are renamed for consistency with all 2M batch models, so `step1000` is the first checkpoint for `pythia-1.4b` that was saved (corresponding to step 500 in training), and `step1000` is likewise the first `pythia-6.9b` checkpoint that was saved (corresponding to 1000 “actual” steps).<br> See [GitHub](https://github.com/EleutherAI/pythia) for more details on training procedure, including [how to reproduce it](https://github.com/EleutherAI/pythia/blob/main/README.md#reproducing-training).<br> Pythia uses the same tokenizer as [GPT-NeoX- 20B](https://huggingface.co/EleutherAI/gpt-neox-20b). ### Evaluations All 16 Pythia models were evaluated using the [LM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness). You can access the results by model and step at `results/json/` in the [GitHub repository](https://github.com/EleutherAI/pythia/tree/main/results/json).<br> Expand the sections below to see plots of evaluation results for all Pythia and Pythia-deduped models compared with OPT and BLOOM. <details> <summary>LAMBADA – OpenAI</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/lambada_openai.png" style="width:auto"/> </details> <details> <summary>Physical Interaction: Question Answering (PIQA)</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/piqa.png" style="width:auto"/> </details> <details> <summary>WinoGrande</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/winogrande.png" style="width:auto"/> </details> <details> <summary>AI2 Reasoning Challenge—Challenge Set</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/arc_challenge.png" style="width:auto"/> </details> <details> <summary>SciQ</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/sciq.png" style="width:auto"/> </details> ### Naming convention and parameter count Pythia* models were renamed in January 2023. It is possible that the old naming convention still persists in some documentation by accident. The current naming convention (70M, 160M, etc.) is based on total parameter count. <figure style="width:32em"> \| current Pythia suffix \| old suffix \| total params \| non-embedding params \| \| --------------------: \| ---------: \| -------------: \| -------------------: \| \| 70M \| 19M \| 70,426,624 \| 18,915,328 \| \| 160M \| 125M \| 162,322,944 \| 85,056,000 \| \| 410M \| 350M \| 405,334,016 \| 302,311,424 \| \| 1B \| 800M \| 1,011,781,632 \| 805,736,448 \| \| 1.4B \| 1.3B \| 1,414,647,808 \| 1,208,602,624 \| \| 2.8B \| 2.7B \| 2,775,208,960 \| 2,517,652,480 \| \| 6.9B \| 6.7B \| 6,857,302,016 \| 6,444,163,072 \| \| 12B \| 13B \| 11,846,072,320 \| 11,327,027,200 \| </figure>
EleutherAI/pythia-70m-deduped-v0	EleutherAI	2023-07-10T01:32:46Z	933	8	transformers	[ "transformers", "pytorch", "safetensors", "gpt_neox", "text-generation", "causal-lm", "pythia", "pythia_v0", "en", "dataset:EleutherAI/the_pile_deduplicated", "arxiv:2101.00027", "arxiv:2201.07311", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-11-01T00:24:53Z	--- language: - en tags: - pytorch - causal-lm - pythia - pythia_v0 license: apache-2.0 datasets: - EleutherAI/the_pile_deduplicated --- The Pythia Scaling Suite is a collection of models developed to facilitate interpretability research. It contains two sets of eight models of sizes 70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two models: one trained on the Pile, and one trained on the Pile after the dataset has been globally deduplicated. All 8 model sizes are trained on the exact same data, in the exact same order. All Pythia models are available [on Hugging Face](https://huggingface.co/models?other=pythia). The Pythia model suite was deliberately designed to promote scientific research on large language models, especially interpretability research. Despite not centering downstream performance as a design goal, we find the models <a href="#evaluations">match or exceed</a> the performance of similar and same-sized models, such as those in the OPT and GPT-Neo suites. Please note that all models in the Pythia suite were renamed in January 2023. For clarity, a <a href="#naming-convention-and-parameter-count">table comparing the old and new names</a> is provided in this model card, together with exact parameter counts. ## Pythia-70M-deduped ### Model Details - Developed by: [EleutherAI](http://eleuther.ai) - Model type: Transformer-based Language Model - Language: English - Learn more: [Pythia's GitHub repository](https://github.com/EleutherAI/pythia) for training procedure, config files, and details on how to use. - Library: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) - License: Apache 2.0 - Contact: to ask questions about this model, join the [EleutherAI Discord](https://discord.gg/zBGx3azzUn), and post them in `#release-discussion`. Please read the existing Pythia documentation before asking about it in the EleutherAI Discord. For general correspondence: [contact@eleuther. ai](mailto:[email protected]). <figure> \| Pythia model \| Non-Embedding Params \| Layers \| Model Dim \| Heads \| Batch Size \| Learning Rate \| Equivalent Models \| \| -----------: \| -------------------: \| :----: \| :-------: \| :---: \| :--------: \| :-------------------: \| :--------------------: \| \| 70M \| 18,915,328 \| 6 \| 512 \| 8 \| 2M \| 1.0 x 10<sup>-3</sup> \| — \| \| 160M \| 85,056,000 \| 12 \| 768 \| 12 \| 4M \| 6.0 x 10<sup>-4</sup> \| GPT-Neo 125M, OPT-125M \| \| 410M \| 302,311,424 \| 24 \| 1024 \| 16 \| 4M \| 3.0 x 10<sup>-4</sup> \| OPT-350M \| \| 1.0B \| 805,736,448 \| 16 \| 2048 \| 8 \| 2M \| 3.0 x 10<sup>-4</sup> \| — \| \| 1.4B \| 1,208,602,624 \| 24 \| 2048 \| 16 \| 4M \| 2.0 x 10<sup>-4</sup> \| GPT-Neo 1.3B, OPT-1.3B \| \| 2.8B \| 2,517,652,480 \| 32 \| 2560 \| 32 \| 2M \| 1.6 x 10<sup>-4</sup> \| GPT-Neo 2.7B, OPT-2.7B \| \| 6.9B \| 6,444,163,072 \| 32 \| 4096 \| 32 \| 2M \| 1.2 x 10<sup>-4</sup> \| OPT-6.7B \| \| 12B \| 11,327,027,200 \| 36 \| 5120 \| 40 \| 2M \| 1.2 x 10<sup>-4</sup> \| — \| <figcaption>Engineering details for the <i>Pythia Suite</i>. Deduped and non-deduped models of a given size have the same hyperparameters. “Equivalent” models have <b>exactly</b> the same architecture, and the same number of non-embedding parameters.</figcaption> </figure> ### Uses and Limitations #### Intended Use The primary intended use of Pythia is research on the behavior, functionality, and limitations of large language models. This suite is intended to provide a controlled setting for performing scientific experiments. To enable the study of how language models change in the course of training, we provide 143 evenly spaced intermediate checkpoints per model. These checkpoints are hosted on Hugging Face as branches. Note that branch `143000` corresponds exactly to the model checkpoint on the `main` branch of each model. You may also further fine-tune and adapt Pythia-70M-deduped for deployment, as long as your use is in accordance with the Apache 2.0 license. Pythia models work with the Hugging Face [Transformers Library](https://huggingface.co/docs/transformers/index). If you decide to use pre-trained Pythia-70M-deduped as a basis for your fine-tuned model, please conduct your own risk and bias assessment. #### Out-of-scope use The Pythia Suite is not intended for deployment. It is not a in itself a product and cannot be used for human-facing interactions. Pythia models are English-language only, and are not suitable for translation or generating text in other languages. Pythia-70M-deduped has not been fine-tuned for downstream contexts in which language models are commonly deployed, such as writing genre prose, or commercial chatbots. This means Pythia-70M-deduped will not respond to a given prompt the way a product like ChatGPT does. This is because, unlike this model, ChatGPT was fine-tuned using methods such as Reinforcement Learning from Human Feedback (RLHF) to better “understand” human instructions. #### Limitations and biases The core functionality of a large language model is to take a string of text and predict the next token. The token deemed statistically most likely by the model need not produce the most “accurate” text. Never rely on Pythia-70M-deduped to produce factually accurate output. This model was trained on [the Pile](https://pile.eleuther.ai/), a dataset known to contain profanity and texts that are lewd or otherwise offensive. See [Section 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a discussion of documented biases with regards to gender, religion, and race. Pythia-70M-deduped may produce socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. If you plan on using text generated through, for example, the Hosted Inference API, we recommend having a human curate the outputs of this language model before presenting it to other people. Please inform your audience that the text was generated by Pythia-70M-deduped. ### Quickstart Pythia models can be loaded and used via the following code, demonstrated here for the third `pythia-70m-deduped` checkpoint: ```python from transformers import GPTNeoXForCausalLM, AutoTokenizer model = GPTNeoXForCausalLM.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) tokenizer = AutoTokenizer.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) inputs = tokenizer("Hello, I am", return_tensors="pt") tokens = model.generate(inputs) tokenizer.decode(tokens[0]) ``` Revision/branch `step143000` corresponds exactly to the model checkpoint on the `main` branch of each model.<br> For more information on how to use all Pythia models, see [documentation on GitHub](https://github.com/EleutherAI/pythia). ### Training #### Training data Pythia-70M-deduped was trained on the Pile after the dataset has been globally deduplicated*.<br> [The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in English. It was created by EleutherAI specifically for training large language models. It contains texts from 22 diverse sources, roughly broken down into five categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl), prose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and miscellaneous (e.g. GitHub, Enron Emails). See [the Pile paper](https://arxiv.org/abs/2101.00027) for a breakdown of all data sources, methodology, and a discussion of ethical implications. Consult [the datasheet](https://arxiv.org/abs/2201.07311) for more detailed documentation about the Pile and its component datasets. The Pile can be downloaded from the [official website](https://pile.eleuther.ai/), or from a [community mirror](https://the-eye.eu/public/AI/pile/). #### Training procedure All models were trained on the exact same data, in the exact same order. Each model saw 299,892,736,000 tokens during training, and 143 checkpoints for each model are saved every 2,097,152,000 tokens, spaced evenly throughout training. This corresponds to training for just under 1 epoch on the Pile for non-deduplicated models, and about 1.5 epochs on the deduplicated Pile. All Pythia* models trained for the equivalent of 143000 steps at a batch size of 2,097,152 tokens. Two batch sizes were used: 2M and 4M. Models with a batch size of 4M tokens listed were originally trained for 71500 steps instead, with checkpoints every 500 steps. The checkpoints on Hugging Face are renamed for consistency with all 2M batch models, so `step1000` is the first checkpoint for `pythia-1.4b` that was saved (corresponding to step 500 in training), and `step1000` is likewise the first `pythia-6.9b` checkpoint that was saved (corresponding to 1000 “actual” steps).<br> See [GitHub](https://github.com/EleutherAI/pythia) for more details on training procedure, including [how to reproduce it](https://github.com/EleutherAI/pythia/blob/main/README.md#reproducing-training).<br> Pythia uses the same tokenizer as [GPT-NeoX- 20B](https://huggingface.co/EleutherAI/gpt-neox-20b). ### Evaluations All 16 Pythia models were evaluated using the [LM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness). You can access the results by model and step at `results/json/` in the [GitHub repository](https://github.com/EleutherAI/pythia/tree/main/results/json).<br> Expand the sections below to see plots of evaluation results for all Pythia and Pythia-deduped models compared with OPT and BLOOM. <details> <summary>LAMBADA – OpenAI</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/lambada_openai.png" style="width:auto"/> </details> <details> <summary>Physical Interaction: Question Answering (PIQA)</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/piqa.png" style="width:auto"/> </details> <details> <summary>WinoGrande</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/winogrande.png" style="width:auto"/> </details> <details> <summary>AI2 Reasoning Challenge – Challenge Set</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/arc_challenge.png" style="width:auto"/> </details> <details> <summary>SciQ</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/sciq.png" style="width:auto"/> </details> ### Naming convention and parameter count Pythia* models were renamed in January 2023. It is possible that the old naming convention still persists in some documentation by accident. The current naming convention (70M, 160M, etc.) is based on total parameter count. <figure style="width:32em"> \| current Pythia suffix \| old suffix \| total params \| non-embedding params \| \| --------------------: \| ---------: \| -------------: \| -------------------: \| \| 70M \| 19M \| 70,426,624 \| 18,915,328 \| \| 160M \| 125M \| 162,322,944 \| 85,056,000 \| \| 410M \| 350M \| 405,334,016 \| 302,311,424 \| \| 1B \| 800M \| 1,011,781,632 \| 805,736,448 \| \| 1.4B \| 1.3B \| 1,414,647,808 \| 1,208,602,624 \| \| 2.8B \| 2.7B \| 2,775,208,960 \| 2,517,652,480 \| \| 6.9B \| 6.7B \| 6,857,302,016 \| 6,444,163,072 \| \| 12B \| 13B \| 11,846,072,320 \| 11,327,027,200 \| </figure>
EleutherAI/pythia-410m-deduped-v0	EleutherAI	2023-07-10T01:31:39Z	860	6	transformers	[ "transformers", "pytorch", "safetensors", "gpt_neox", "text-generation", "causal-lm", "pythia", "pythia_v0", "en", "dataset:EleutherAI/the_pile_deduplicated", "arxiv:2101.00027", "arxiv:2201.07311", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-11-01T00:48:44Z	--- language: - en tags: - pytorch - causal-lm - pythia - pythia_v0 license: apache-2.0 datasets: - EleutherAI/the_pile_deduplicated --- The Pythia Scaling Suite is a collection of models developed to facilitate interpretability research. It contains two sets of eight models of sizes 70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two models: one trained on the Pile, and one trained on the Pile after the dataset has been globally deduplicated. All 8 model sizes are trained on the exact same data, in the exact same order. All Pythia models are available [on Hugging Face](https://huggingface.co/models?other=pythia). The Pythia model suite was deliberately designed to promote scientific research on large language models, especially interpretability research. Despite not centering downstream performance as a design goal, we find the models <a href="#evaluations">match or exceed</a> the performance of similar and same-sized models, such as those in the OPT and GPT-Neo suites. Please note that all models in the Pythia suite were renamed in January 2023. For clarity, a <a href="#naming-convention-and-parameter-count">table comparing the old and new names</a> is provided in this model card, together with exact parameter counts. ## Pythia-410M-deduped ### Model Details - Developed by: [EleutherAI](http://eleuther.ai) - Model type: Transformer-based Language Model - Language: English - Learn more: [Pythia's GitHub repository](https://github.com/EleutherAI/pythia) for training procedure, config files, and details on how to use. - Library: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) - License: Apache 2.0 - Contact: to ask questions about this model, join the [EleutherAI Discord](https://discord.gg/zBGx3azzUn), and post them in `#release-discussion`. Please read the existing Pythia documentation before asking about it in the EleutherAI Discord. For general correspondence: [contact@eleuther. ai](mailto:[email protected]). <figure> \| Pythia model \| Non-Embedding Params \| Layers \| Model Dim \| Heads \| Batch Size \| Learning Rate \| Equivalent Models \| \| -----------: \| -------------------: \| :----: \| :-------: \| :---: \| :--------: \| :-------------------: \| :--------------------: \| \| 70M \| 18,915,328 \| 6 \| 512 \| 8 \| 2M \| 1.0 x 10<sup>-3</sup> \| — \| \| 160M \| 85,056,000 \| 12 \| 768 \| 12 \| 4M \| 6.0 x 10<sup>-4</sup> \| GPT-Neo 125M, OPT-125M \| \| 410M \| 302,311,424 \| 24 \| 1024 \| 16 \| 4M \| 3.0 x 10<sup>-4</sup> \| OPT-350M \| \| 1.0B \| 805,736,448 \| 16 \| 2048 \| 8 \| 2M \| 3.0 x 10<sup>-4</sup> \| — \| \| 1.4B \| 1,208,602,624 \| 24 \| 2048 \| 16 \| 4M \| 2.0 x 10<sup>-4</sup> \| GPT-Neo 1.3B, OPT-1.3B \| \| 2.8B \| 2,517,652,480 \| 32 \| 2560 \| 32 \| 2M \| 1.6 x 10<sup>-4</sup> \| GPT-Neo 2.7B, OPT-2.7B \| \| 6.9B \| 6,444,163,072 \| 32 \| 4096 \| 32 \| 2M \| 1.2 x 10<sup>-4</sup> \| OPT-6.7B \| \| 12B \| 11,327,027,200 \| 36 \| 5120 \| 40 \| 2M \| 1.2 x 10<sup>-4</sup> \| — \| <figcaption>Engineering details for the <i>Pythia Suite</i>. Deduped and non-deduped models of a given size have the same hyperparameters. “Equivalent” models have <b>exactly</b> the same architecture, and the same number of non-embedding parameters.</figcaption> </figure> ### Uses and Limitations #### Intended Use The primary intended use of Pythia is research on the behavior, functionality, and limitations of large language models. This suite is intended to provide a controlled setting for performing scientific experiments. To enable the study of how language models change in the course of training, we provide 143 evenly spaced intermediate checkpoints per model. These checkpoints are hosted on Hugging Face as branches. Note that branch `143000` corresponds exactly to the model checkpoint on the `main` branch of each model. You may also further fine-tune and adapt Pythia-410M-deduped for deployment, as long as your use is in accordance with the Apache 2.0 license. Pythia models work with the Hugging Face [Transformers Library](https://huggingface.co/docs/transformers/index). If you decide to use pre-trained Pythia-410M-deduped as a basis for your fine-tuned model, please conduct your own risk and bias assessment. #### Out-of-scope use The Pythia Suite is not intended for deployment. It is not a in itself a product and cannot be used for human-facing interactions. Pythia models are English-language only, and are not suitable for translation or generating text in other languages. Pythia-410M-deduped has not been fine-tuned for downstream contexts in which language models are commonly deployed, such as writing genre prose, or commercial chatbots. This means Pythia-410M-deduped will not respond to a given prompt the way a product like ChatGPT does. This is because, unlike this model, ChatGPT was fine-tuned using methods such as Reinforcement Learning from Human Feedback (RLHF) to better “understand” human instructions. #### Limitations and biases The core functionality of a large language model is to take a string of text and predict the next token. The token deemed statistically most likely by the model need not produce the most “accurate” text. Never rely on Pythia-410M-deduped to produce factually accurate output. This model was trained on [the Pile](https://pile.eleuther.ai/), a dataset known to contain profanity and texts that are lewd or otherwise offensive. See [Section 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a discussion of documented biases with regards to gender, religion, and race. Pythia-410M-deduped may produce socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. If you plan on using text generated through, for example, the Hosted Inference API, we recommend having a human curate the outputs of this language model before presenting it to other people. Please inform your audience that the text was generated by Pythia-410M-deduped. ### Quickstart Pythia models can be loaded and used via the following code, demonstrated here for the third `pythia-70m-deduped` checkpoint: ```python from transformers import GPTNeoXForCausalLM, AutoTokenizer model = GPTNeoXForCausalLM.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) tokenizer = AutoTokenizer.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) inputs = tokenizer("Hello, I am", return_tensors="pt") tokens = model.generate(inputs) tokenizer.decode(tokens[0]) ``` Revision/branch `step143000` corresponds exactly to the model checkpoint on the `main` branch of each model.<br> For more information on how to use all Pythia models, see [documentation on GitHub](https://github.com/EleutherAI/pythia). ### Training #### Training data Pythia-410M-deduped was trained on the Pile after the dataset has been globally deduplicated*.<br> [The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in English. It was created by EleutherAI specifically for training large language models. It contains texts from 22 diverse sources, roughly broken down into five categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl), prose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and miscellaneous (e.g. GitHub, Enron Emails). See [the Pile paper](https://arxiv.org/abs/2101.00027) for a breakdown of all data sources, methodology, and a discussion of ethical implications. Consult [the datasheet](https://arxiv.org/abs/2201.07311) for more detailed documentation about the Pile and its component datasets. The Pile can be downloaded from the [official website](https://pile.eleuther.ai/), or from a [community mirror](https://the-eye.eu/public/AI/pile/). #### Training procedure All models were trained on the exact same data, in the exact same order. Each model saw 299,892,736,000 tokens during training, and 143 checkpoints for each model are saved every 2,097,152,000 tokens, spaced evenly throughout training. This corresponds to training for just under 1 epoch on the Pile for non-deduplicated models, and about 1.5 epochs on the deduplicated Pile. All Pythia* models trained for the equivalent of 143000 steps at a batch size of 2,097,152 tokens. Two batch sizes were used: 2M and 4M. Models with a batch size of 4M tokens listed were originally trained for 71500 steps instead, with checkpoints every 500 steps. The checkpoints on Hugging Face are renamed for consistency with all 2M batch models, so `step1000` is the first checkpoint for `pythia-1.4b` that was saved (corresponding to step 500 in training), and `step1000` is likewise the first `pythia-6.9b` checkpoint that was saved (corresponding to 1000 “actual” steps).<br> See [GitHub](https://github.com/EleutherAI/pythia) for more details on training procedure, including [how to reproduce it](https://github.com/EleutherAI/pythia/blob/main/README.md#reproducing-training).<br> Pythia uses the same tokenizer as [GPT-NeoX- 20B](https://huggingface.co/EleutherAI/gpt-neox-20b). ### Evaluations All 16 Pythia models were evaluated using the [LM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness). You can access the results by model and step at `results/json/` in the [GitHub repository](https://github.com/EleutherAI/pythia/tree/main/results/json).<br> Expand the sections below to see plots of evaluation results for all Pythia and Pythia-deduped models compared with OPT and BLOOM. <details> <summary>LAMBADA – OpenAI</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/lambada_openai.png" style="width:auto"/> </details> <details> <summary>Physical Interaction: Question Answering (PIQA)</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/piqa.png" style="width:auto"/> </details> <details> <summary>WinoGrande</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/winogrande.png" style="width:auto"/> </details> <details> <summary>AI2 Reasoning Challenge – Challenge Set</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/arc_challenge.png" style="width:auto"/> </details> <details> <summary>SciQ</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/sciq.png" style="width:auto"/> </details> ### Naming convention and parameter count Pythia* models were renamed in January 2023. It is possible that the old naming convention still persists in some documentation by accident. The current naming convention (70M, 160M, etc.) is based on total parameter count. <figure style="width:32em"> \| current Pythia suffix \| old suffix \| total params \| non-embedding params \| \| --------------------: \| ---------: \| -------------: \| -------------------: \| \| 70M \| 19M \| 70,426,624 \| 18,915,328 \| \| 160M \| 125M \| 162,322,944 \| 85,056,000 \| \| 410M \| 350M \| 405,334,016 \| 302,311,424 \| \| 1B \| 800M \| 1,011,781,632 \| 805,736,448 \| \| 1.4B \| 1.3B \| 1,414,647,808 \| 1,208,602,624 \| \| 2.8B \| 2.7B \| 2,775,208,960 \| 2,517,652,480 \| \| 6.9B \| 6.7B \| 6,857,302,016 \| 6,444,163,072 \| \| 12B \| 13B \| 11,846,072,320 \| 11,327,027,200 \| </figure>
EleutherAI/pythia-160m-deduped-v0	EleutherAI	2023-07-10T01:30:40Z	847	6	transformers	[ "transformers", "pytorch", "safetensors", "gpt_neox", "text-generation", "causal-lm", "pythia", "pythia_v0", "en", "dataset:EleutherAI/the_pile_deduplicated", "arxiv:2101.00027", "arxiv:2201.07311", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-10-18T02:59:41Z	--- language: - en tags: - pytorch - causal-lm - pythia - pythia_v0 license: apache-2.0 datasets: - EleutherAI/the_pile_deduplicated --- The Pythia Scaling Suite is a collection of models developed to facilitate interpretability research. It contains two sets of eight models of sizes 70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two models: one trained on the Pile, and one trained on the Pile after the dataset has been globally deduplicated. All 8 model sizes are trained on the exact same data, in the exact same order. All Pythia models are available [on Hugging Face](https://huggingface.co/models?other=pythia). The Pythia model suite was deliberately designed to promote scientific research on large language models, especially interpretability research. Despite not centering downstream performance as a design goal, we find the models <a href="#evaluations">match or exceed</a> the performance of similar and same-sized models, such as those in the OPT and GPT-Neo suites. Please note that all models in the Pythia suite were renamed in January 2023. For clarity, a <a href="#naming-convention-and-parameter-count">table comparing the old and new names</a> is provided in this model card, together with exact parameter counts. ## Pythia-160M-deduped ### Model Details - Developed by: [EleutherAI](http://eleuther.ai) - Model type: Transformer-based Language Model - Language: English - Learn more: [Pythia's GitHub repository](https://github.com/EleutherAI/pythia) for training procedure, config files, and details on how to use. - Library: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) - License: Apache 2.0 - Contact: to ask questions about this model, join the [EleutherAI Discord](https://discord.gg/zBGx3azzUn), and post them in `#release-discussion`. Please read the existing Pythia documentation before asking about it in the EleutherAI Discord. For general correspondence: [contact@eleuther. ai](mailto:[email protected]). <figure> \| Pythia model \| Non-Embedding Params \| Layers \| Model Dim \| Heads \| Batch Size \| Learning Rate \| Equivalent Models \| \| -----------: \| -------------------: \| :----: \| :-------: \| :---: \| :--------: \| :-------------------: \| :--------------------: \| \| 70M \| 18,915,328 \| 6 \| 512 \| 8 \| 2M \| 1.0 x 10<sup>-3</sup> \| — \| \| 160M \| 85,056,000 \| 12 \| 768 \| 12 \| 4M \| 6.0 x 10<sup>-4</sup> \| GPT-Neo 125M, OPT-125M \| \| 410M \| 302,311,424 \| 24 \| 1024 \| 16 \| 4M \| 3.0 x 10<sup>-4</sup> \| OPT-350M \| \| 1.0B \| 805,736,448 \| 16 \| 2048 \| 8 \| 2M \| 3.0 x 10<sup>-4</sup> \| — \| \| 1.4B \| 1,208,602,624 \| 24 \| 2048 \| 16 \| 4M \| 2.0 x 10<sup>-4</sup> \| GPT-Neo 1.3B, OPT-1.3B \| \| 2.8B \| 2,517,652,480 \| 32 \| 2560 \| 32 \| 2M \| 1.6 x 10<sup>-4</sup> \| GPT-Neo 2.7B, OPT-2.7B \| \| 6.9B \| 6,444,163,072 \| 32 \| 4096 \| 32 \| 2M \| 1.2 x 10<sup>-4</sup> \| OPT-6.7B \| \| 12B \| 11,327,027,200 \| 36 \| 5120 \| 40 \| 2M \| 1.2 x 10<sup>-4</sup> \| — \| <figcaption>Engineering details for the <i>Pythia Suite</i>. Deduped and non-deduped models of a given size have the same hyperparameters. “Equivalent” models have <b>exactly</b> the same architecture, and the same number of non-embedding parameters.</figcaption> </figure> ### Uses and Limitations #### Intended Use The primary intended use of Pythia is research on the behavior, functionality, and limitations of large language models. This suite is intended to provide a controlled setting for performing scientific experiments. To enable the study of how language models change in the course of training, we provide 143 evenly spaced intermediate checkpoints per model. These checkpoints are hosted on Hugging Face as branches. Note that branch `143000` corresponds exactly to the model checkpoint on the `main` branch of each model. You may also further fine-tune and adapt Pythia-160M-deduped for deployment, as long as your use is in accordance with the Apache 2.0 license. Pythia models work with the Hugging Face [Transformers Library](https://huggingface.co/docs/transformers/index). If you decide to use pre-trained Pythia-160M-deduped as a basis for your fine-tuned model, please conduct your own risk and bias assessment. #### Out-of-scope use The Pythia Suite is not intended for deployment. It is not a in itself a product and cannot be used for human-facing interactions. Pythia models are English-language only, and are not suitable for translation or generating text in other languages. Pythia-160M-deduped has not been fine-tuned for downstream contexts in which language models are commonly deployed, such as writing genre prose, or commercial chatbots. This means Pythia-160M-deduped will not respond to a given prompt the way a product like ChatGPT does. This is because, unlike this model, ChatGPT was fine-tuned using methods such as Reinforcement Learning from Human Feedback (RLHF) to better “understand” human instructions. #### Limitations and biases The core functionality of a large language model is to take a string of text and predict the next token. The token deemed statistically most likely by the model need not produce the most “accurate” text. Never rely on Pythia-160M-deduped to produce factually accurate output. This model was trained on [the Pile](https://pile.eleuther.ai/), a dataset known to contain profanity and texts that are lewd or otherwise offensive. See [Section 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a discussion of documented biases with regards to gender, religion, and race. Pythia-160M-deduped may produce socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. If you plan on using text generated through, for example, the Hosted Inference API, we recommend having a human curate the outputs of this language model before presenting it to other people. Please inform your audience that the text was generated by Pythia-160M-deduped. ### Quickstart Pythia models can be loaded and used via the following code, demonstrated here for the third `pythia-70m-deduped` checkpoint: ```python from transformers import GPTNeoXForCausalLM, AutoTokenizer model = GPTNeoXForCausalLM.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) tokenizer = AutoTokenizer.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) inputs = tokenizer("Hello, I am", return_tensors="pt") tokens = model.generate(inputs) tokenizer.decode(tokens[0]) ``` Revision/branch `step143000` corresponds exactly to the model checkpoint on the `main` branch of each model.<br> For more information on how to use all Pythia models, see [documentation on GitHub](https://github.com/EleutherAI/pythia). ### Training #### Training data Pythia-160M-deduped was trained on the Pile after the dataset has been globally deduplicated*.<br> [The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in English. It was created by EleutherAI specifically for training large language models. It contains texts from 22 diverse sources, roughly broken down into five categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl), prose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and miscellaneous (e.g. GitHub, Enron Emails). See [the Pile paper](https://arxiv.org/abs/2101.00027) for a breakdown of all data sources, methodology, and a discussion of ethical implications. Consult [the datasheet](https://arxiv.org/abs/2201.07311) for more detailed documentation about the Pile and its component datasets. The Pile can be downloaded from the [official website](https://pile.eleuther.ai/), or from a [community mirror](https://the-eye.eu/public/AI/pile/). #### Training procedure All models were trained on the exact same data, in the exact same order. Each model saw 299,892,736,000 tokens during training, and 143 checkpoints for each model are saved every 2,097,152,000 tokens, spaced evenly throughout training. This corresponds to training for just under 1 epoch on the Pile for non-deduplicated models, and about 1.5 epochs on the deduplicated Pile. All Pythia* models trained for the equivalent of 143000 steps at a batch size of 2,097,152 tokens. Two batch sizes were used: 2M and 4M. Models with a batch size of 4M tokens listed were originally trained for 71500 steps instead, with checkpoints every 500 steps. The checkpoints on Hugging Face are renamed for consistency with all 2M batch models, so `step1000` is the first checkpoint for `pythia-1.4b` that was saved (corresponding to step 500 in training), and `step1000` is likewise the first `pythia-6.9b` checkpoint that was saved (corresponding to 1000 “actual” steps).<br> See [GitHub](https://github.com/EleutherAI/pythia) for more details on training procedure, including [how to reproduce it](https://github.com/EleutherAI/pythia/blob/main/README.md#reproducing-training).<br> Pythia uses the same tokenizer as [GPT-NeoX- 20B](https://huggingface.co/EleutherAI/gpt-neox-20b). ### Evaluations All 16 Pythia models were evaluated using the [LM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness). You can access the results by model and step at `results/json/` in the [GitHub repository](https://github.com/EleutherAI/pythia/tree/main/results/json).<br> Expand the sections below to see plots of evaluation results for all Pythia and Pythia-deduped models compared with OPT and BLOOM. <details> <summary>LAMBADA – OpenAI</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/lambada_openai.png" style="width:auto"/> </details> <details> <summary>Physical Interaction: Question Answering (PIQA)</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/piqa.png" style="width:auto"/> </details> <details> <summary>WinoGrande</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/winogrande.png" style="width:auto"/> </details> <details> <summary>AI2 Reasoning Challenge – Challenge Set</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/arc_challenge.png" style="width:auto"/> </details> <details> <summary>SciQ</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/sciq.png" style="width:auto"/> </details> ### Naming convention and parameter count Pythia* models were renamed in January 2023. It is possible that the old naming convention still persists in some documentation by accident. The current naming convention (70M, 160M, etc.) is based on total parameter count. <figure style="width:32em"> \| current Pythia suffix \| old suffix \| total params \| non-embedding params \| \| --------------------: \| ---------: \| -------------: \| -------------------: \| \| 70M \| 19M \| 70,426,624 \| 18,915,328 \| \| 160M \| 125M \| 162,322,944 \| 85,056,000 \| \| 410M \| 350M \| 405,334,016 \| 302,311,424 \| \| 1B \| 800M \| 1,011,781,632 \| 805,736,448 \| \| 1.4B \| 1.3B \| 1,414,647,808 \| 1,208,602,624 \| \| 2.8B \| 2.7B \| 2,775,208,960 \| 2,517,652,480 \| \| 6.9B \| 6.7B \| 6,857,302,016 \| 6,444,163,072 \| \| 12B \| 13B \| 11,846,072,320 \| 11,327,027,200 \| </figure>
EleutherAI/pythia-6.9b-deduped-v0	EleutherAI	2023-07-10T01:30:05Z	833	20	transformers	[ "transformers", "pytorch", "gpt_neox", "text-generation", "causal-lm", "pythia", "pythia_v0", "en", "dataset:EleutherAI/the_pile_deduplicated", "arxiv:2101.00027", "arxiv:2201.07311", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-10-18T03:04:37Z	--- language: - en tags: - pytorch - causal-lm - pythia - pythia_v0 license: apache-2.0 datasets: - EleutherAI/the_pile_deduplicated --- The Pythia Scaling Suite is a collection of models developed to facilitate interpretability research. It contains two sets of eight models of sizes 70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two models: one trained on the Pile, and one trained on the Pile after the dataset has been globally deduplicated. All 8 model sizes are trained on the exact same data, in the exact same order. All Pythia models are available [on Hugging Face](https://huggingface.co/models?other=pythia). The Pythia model suite was deliberately designed to promote scientific research on large language models, especially interpretability research. Despite not centering downstream performance as a design goal, we find the models <a href="#evaluations">match or exceed</a> the performance of similar and same-sized models, such as those in the OPT and GPT-Neo suites. Please note that all models in the Pythia suite were renamed in January 2023. For clarity, a <a href="#naming-convention-and-parameter-count">table comparing the old and new names</a> is provided in this model card, together with exact parameter counts. ## Pythia-6.9B-deduped ### Model Details - Developed by: [EleutherAI](http://eleuther.ai) - Model type: Transformer-based Language Model - Language: English - Learn more: [Pythia's GitHub repository](https://github.com/EleutherAI/pythia) for training procedure, config files, and details on how to use. - Library: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) - License: Apache 2.0 - Contact: to ask questions about this model, join the [EleutherAI Discord](https://discord.gg/zBGx3azzUn), and post them in `#release-discussion`. Please read the existing Pythia documentation before asking about it in the EleutherAI Discord. For general correspondence: [contact@eleuther. ai](mailto:[email protected]). <figure> \| Pythia model \| Non-Embedding Params \| Layers \| Model Dim \| Heads \| Batch Size \| Learning Rate \| Equivalent Models \| \| -----------: \| -------------------: \| :----: \| :-------: \| :---: \| :--------: \| :-------------------: \| :--------------------: \| \| 70M \| 18,915,328 \| 6 \| 512 \| 8 \| 2M \| 1.0 x 10<sup>-3</sup> \| — \| \| 160M \| 85,056,000 \| 12 \| 768 \| 12 \| 4M \| 6.0 x 10<sup>-4</sup> \| GPT-Neo 125M, OPT-125M \| \| 410M \| 302,311,424 \| 24 \| 1024 \| 16 \| 4M \| 3.0 x 10<sup>-4</sup> \| OPT-350M \| \| 1.0B \| 805,736,448 \| 16 \| 2048 \| 8 \| 2M \| 3.0 x 10<sup>-4</sup> \| — \| \| 1.4B \| 1,208,602,624 \| 24 \| 2048 \| 16 \| 4M \| 2.0 x 10<sup>-4</sup> \| GPT-Neo 1.3B, OPT-1.3B \| \| 2.8B \| 2,517,652,480 \| 32 \| 2560 \| 32 \| 2M \| 1.6 x 10<sup>-4</sup> \| GPT-Neo 2.7B, OPT-2.7B \| \| 6.9B \| 6,444,163,072 \| 32 \| 4096 \| 32 \| 2M \| 1.2 x 10<sup>-4</sup> \| OPT-6.7B \| \| 12B \| 11,327,027,200 \| 36 \| 5120 \| 40 \| 2M \| 1.2 x 10<sup>-4</sup> \| — \| <figcaption>Engineering details for the <i>Pythia Suite</i>. Deduped and non-deduped models of a given size have the same hyperparameters. “Equivalent” models have <b>exactly</b> the same architecture, and the same number of non-embedding parameters.</figcaption> </figure> ### Uses and Limitations #### Intended Use The primary intended use of Pythia is research on the behavior, functionality, and limitations of large language models. This suite is intended to provide a controlled setting for performing scientific experiments. To enable the study of how language models change in the course of training, we provide 143 evenly spaced intermediate checkpoints per model. These checkpoints are hosted on Hugging Face as branches. Note that branch `143000` corresponds exactly to the model checkpoint on the `main` branch of each model. You may also further fine-tune and adapt Pythia-6.9B-deduped for deployment, as long as your use is in accordance with the Apache 2.0 license. Pythia models work with the Hugging Face [Transformers Library](https://huggingface.co/docs/transformers/index). If you decide to use pre-trained Pythia-6.9B-deduped as a basis for your fine-tuned model, please conduct your own risk and bias assessment. #### Out-of-scope use The Pythia Suite is not intended for deployment. It is not a in itself a product and cannot be used for human-facing interactions. Pythia models are English-language only, and are not suitable for translation or generating text in other languages. Pythia-6.9B-deduped has not been fine-tuned for downstream contexts in which language models are commonly deployed, such as writing genre prose, or commercial chatbots. This means Pythia-6.9B-deduped will not respond to a given prompt the way a product like ChatGPT does. This is because, unlike this model, ChatGPT was fine-tuned using methods such as Reinforcement Learning from Human Feedback (RLHF) to better “understand” human instructions. #### Limitations and biases The core functionality of a large language model is to take a string of text and predict the next token. The token deemed statistically most likely by the model need not produce the most “accurate” text. Never rely on Pythia-6.9B-deduped to produce factually accurate output. This model was trained on [the Pile](https://pile.eleuther.ai/), a dataset known to contain profanity and texts that are lewd or otherwise offensive. See [Section 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a discussion of documented biases with regards to gender, religion, and race. Pythia-6.9B-deduped may produce socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. If you plan on using text generated through, for example, the Hosted Inference API, we recommend having a human curate the outputs of this language model before presenting it to other people. Please inform your audience that the text was generated by Pythia-6.9B-deduped. ### Quickstart Pythia models can be loaded and used via the following code, demonstrated here for the third `pythia-70m-deduped` checkpoint: ```python from transformers import GPTNeoXForCausalLM, AutoTokenizer model = GPTNeoXForCausalLM.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) tokenizer = AutoTokenizer.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) inputs = tokenizer("Hello, I am", return_tensors="pt") tokens = model.generate(inputs) tokenizer.decode(tokens[0]) ``` Revision/branch `step143000` corresponds exactly to the model checkpoint on the `main` branch of each model.<br> For more information on how to use all Pythia models, see [documentation on GitHub](https://github.com/EleutherAI/pythia). ### Training #### Training data Pythia-6.9B-deduped was trained on the Pile after the dataset has been globally deduplicated*.<br> [The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in English. It was created by EleutherAI specifically for training large language models. It contains texts from 22 diverse sources, roughly broken down into five categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl), prose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and miscellaneous (e.g. GitHub, Enron Emails). See [the Pile paper](https://arxiv.org/abs/2101.00027) for a breakdown of all data sources, methodology, and a discussion of ethical implications. Consult [the datasheet](https://arxiv.org/abs/2201.07311) for more detailed documentation about the Pile and its component datasets. The Pile can be downloaded from the [official website](https://pile.eleuther.ai/), or from a [community mirror](https://the-eye.eu/public/AI/pile/). #### Training procedure All models were trained on the exact same data, in the exact same order. Each model saw 299,892,736,000 tokens during training, and 143 checkpoints for each model are saved every 2,097,152,000 tokens, spaced evenly throughout training. This corresponds to training for just under 1 epoch on the Pile for non-deduplicated models, and about 1.5 epochs on the deduplicated Pile. All Pythia* models trained for the equivalent of 143000 steps at a batch size of 2,097,152 tokens. Two batch sizes were used: 2M and 4M. Models with a batch size of 4M tokens listed were originally trained for 71500 steps instead, with checkpoints every 500 steps. The checkpoints on Hugging Face are renamed for consistency with all 2M batch models, so `step1000` is the first checkpoint for `pythia-1.4b` that was saved (corresponding to step 500 in training), and `step1000` is likewise the first `pythia-6.9b` checkpoint that was saved (corresponding to 1000 “actual” steps).<br> See [GitHub](https://github.com/EleutherAI/pythia) for more details on training procedure, including [how to reproduce it](https://github.com/EleutherAI/pythia/blob/main/README.md#reproducing-training).<br> Pythia uses the same tokenizer as [GPT-NeoX- 20B](https://huggingface.co/EleutherAI/gpt-neox-20b). ### Evaluations All 16 Pythia models were evaluated using the [LM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness). You can access the results by model and step at `results/json/` in the [GitHub repository](https://github.com/EleutherAI/pythia/tree/main/results/json).<br> Expand the sections below to see plots of evaluation results for all Pythia and Pythia-deduped models compared with OPT and BLOOM. <details> <summary>LAMBADA – OpenAI</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/lambada_openai.png" style="width:auto"/> </details> <details> <summary>Physical Interaction: Question Answering (PIQA)</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/piqa.png" style="width:auto"/> </details> <details> <summary>WinoGrande</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/winogrande.png" style="width:auto"/> </details> <details> <summary>AI2 Reasoning Challenge – Challenge Set</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/arc_challenge.png" style="width:auto"/> </details> <details> <summary>SciQ</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/sciq.png" style="width:auto"/> </details> ### Naming convention and parameter count Pythia* models were renamed in January 2023. It is possible that the old naming convention still persists in some documentation by accident. The current naming convention (70M, 160M, etc.) is based on total parameter count. <figure style="width:32em"> \| current Pythia suffix \| old suffix \| total params \| non-embedding params \| \| --------------------: \| ---------: \| -------------: \| -------------------: \| \| 70M \| 19M \| 70,426,624 \| 18,915,328 \| \| 160M \| 125M \| 162,322,944 \| 85,056,000 \| \| 410M \| 350M \| 405,334,016 \| 302,311,424 \| \| 1B \| 800M \| 1,011,781,632 \| 805,736,448 \| \| 1.4B \| 1.3B \| 1,414,647,808 \| 1,208,602,624 \| \| 2.8B \| 2.7B \| 2,775,208,960 \| 2,517,652,480 \| \| 6.9B \| 6.7B \| 6,857,302,016 \| 6,444,163,072 \| \| 13B \| 12B \| 11,846,072,320 \| 11,327,027,200 \| </figure>
havingAfish/CDA	havingAfish	2023-07-10T01:00:15Z	0	0	null	[ "dataset:movie_rationales", "region:us" ]	null	2023-07-10T00:59:26Z	--- datasets: - movie_rationales ---
crisU8/bert-finetuned-ner-clinical-trials-2	crisU8	2023-07-10T00:48:07Z	101	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "token-classification", "generated_from_trainer", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-07-10T00:29:03Z	--- license: cc-by-nc-4.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner-clinical-trials-2 results: [] --- <!-- 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-clinical-trials-2 This model is a fine-tuned version of [lcampillos/roberta-es-clinical-trials-ner](https://huggingface.co/lcampillos/roberta-es-clinical-trials-ner) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3516 - Precision: 0.7329 - Recall: 0.7755 - F1: 0.7536 - Accuracy: 0.9141 ## 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: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.158 \| 1.0 \| 502 \| 0.3028 \| 0.7104 \| 0.7700 \| 0.7390 \| 0.9144 \| \| 0.1061 \| 2.0 \| 1004 \| 0.3578 \| 0.7020 \| 0.7772 \| 0.7377 \| 0.9094 \| \| 0.123 \| 3.0 \| 1506 \| 0.3296 \| 0.7237 \| 0.7750 \| 0.7485 \| 0.9139 \| \| 0.1007 \| 4.0 \| 2008 \| 0.3516 \| 0.7329 \| 0.7755 \| 0.7536 \| 0.9141 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
LongshenOu/lyric-trans-en2zh	LongshenOu	2023-07-10T00:16:10Z	31	0	transformers	[ "transformers", "pytorch", "mbart", "license:cc-by-nc-sa-4.0", "endpoints_compatible", "region:us" ]	null	2023-07-10T00:07:39Z	--- license: cc-by-nc-sa-4.0 ---
NasimB/gpt2-concat-guten-rarity-all-mod-repetition-iorder-5k-p5k	NasimB	2023-07-10T00:11:09Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-09T22:15:33Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: gpt2-concat-guten-rarity-all-mod-repetition-iorder-5k-p5k results: [] --- <!-- 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. --> # gpt2-concat-guten-rarity-all-mod-repetition-iorder-5k-p5k This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 3.1812 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.7049 \| 0.3 \| 500 \| 5.6332 \| \| 5.3603 \| 0.59 \| 1000 \| 5.2033 \| \| 5.0063 \| 0.89 \| 1500 \| 4.9509 \| \| 4.7286 \| 1.18 \| 2000 \| 4.7987 \| \| 4.5752 \| 1.48 \| 2500 \| 4.6728 \| \| 4.4634 \| 1.78 \| 3000 \| 4.5663 \| \| 4.3226 \| 2.07 \| 3500 \| 4.4933 \| \| 4.1472 \| 2.37 \| 4000 \| 4.4458 \| \| 4.1157 \| 2.67 \| 4500 \| 4.3824 \| \| 4.0756 \| 2.96 \| 5000 \| 4.3282 \| \| 3.8402 \| 3.26 \| 5500 \| 4.3258 \| \| 3.8183 \| 3.55 \| 6000 \| 4.2905 \| \| 3.7968 \| 3.85 \| 6500 \| 4.2597 \| \| 3.6538 \| 4.15 \| 7000 \| 4.2640 \| \| 3.5239 \| 4.44 \| 7500 \| 4.2506 \| \| 3.5235 \| 4.74 \| 8000 \| 4.2375 \| \| 3.4943 \| 5.04 \| 8500 \| 4.2350 \| \| 3.3327 \| 5.33 \| 9000 \| 4.2405 \| \| 3.3319 \| 5.63 \| 9500 \| 4.2383 \| \| 3.3325 \| 5.92 \| 10000 \| 4.2378 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

1aurent/q-Taxi-v3

1aurent

2023-07-10T13:25:59Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-07-10T13:02:36Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="1aurent/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

Neko-Institute-of-Science/guanaco-unchained-33b-qlora

Neko-Institute-of-Science

2023-07-10T13:24:08Z

0

3

null

[ "dataset:CheshireAI/guanaco-unchained", "region:us" ]

null

2023-07-10T00:10:04Z

--- datasets: - CheshireAI/guanaco-unchained --- Let's see how this goes. Training in 8 bit and at full context. Is 8bit even a qlora? ``` python qlora.py \ --model_name_or_path /UI/text-generation-webui/models/llama-30b \ --output_dir ./output/guanaco-33b \ --logging_steps 1 \ --save_strategy steps \ --data_seed 42 \ --save_steps 69 \ --save_total_limit 999 \ --per_device_eval_batch_size 1 \ --dataloader_num_workers 3 \ --group_by_length \ --logging_strategy steps \ --remove_unused_columns False \ --do_train \ --do_eval false \ --do_mmlu_eval false \ --lora_r 64 \ --lora_alpha 16 \ --lora_modules all \ --bf16 \ --bits 8 \ --warmup_ratio 0.03 \ --lr_scheduler_type constant \ --gradient_checkpointing \ --gradient_accumulation_steps 32 \ --dataset oasst1 \ --source_max_len 2048 \ --target_max_len 2048 \ --per_device_train_batch_size 1 \ --num_train_epochs 3 \ --learning_rate 0.0001 \ --adam_beta2 0.999 \ --max_grad_norm 0.3 \ --lora_dropout 0.05 \ --weight_decay 0.0 \ --seed 0 ```

TheBloke/Chronoboros-33B-GGML

TheBloke

2023-07-10T13:16:31Z

0

11

null

[ "license:other", "region:us" ]

null

2023-07-10T08:29:30Z

--- inference: false license: other ---  <div style="width: 100%;"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <a href="https://discord.gg/theblokeai">Chat & support: my new Discord server</a> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a> </div> </div>  # Henk717's Chronoboros 33B GGML These files are GGML format model files for [Henk717's Chronoboros 33B](https://huggingface.co/Henk717/chronoboros-33B). GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as: * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a powerful GGML web UI with full GPU acceleration out of the box. Especially good for story telling. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with GPU acceleration via the c_transformers backend. * [LM Studio](https://lmstudio.ai/), a fully featured local GUI. Supports full GPU accel on macOS. Also supports Windows, without GPU accel. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most popular web UI. Requires extra steps to enable GPU accel via llama.cpp backend. * [ctransformers](https://github.com/marella/ctransformers), a Python library with LangChain support and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with OpenAI-compatible API server. ## Repositories available * [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/Chronoboros-33B-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/Chronoboros-33B-GGML) * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Henk717/chronoboros-33B) ## Prompt template: Alpaca ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ```  ## Compatibility ### Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0` These are guaranteed to be compatible with any UIs, tools and libraries released since late May. They may be phased out soon, as they are largely superseded by the new k-quant methods. ### New k-quant methods: `q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K` These new quantisation methods are compatible with llama.cpp as of June 6th, commit `2d43387`. They are now also compatible with recent releases of text-generation-webui, KoboldCpp, llama-cpp-python, ctransformers, rustformers and most others. For compatibility with other tools and libraries, please check their documentation. ## Explanation of the new k-quant methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw * GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type. Refer to the Provided Files table below to see what files use which methods, and how. </details>  ## Provided files | Name | Quant method | Bits | Size | Max RAM required | Use case | | ---- | ---- | ---- | ---- | ---- | ----- | | chronoboros-33b.ggmlv3.q2_K.bin | q2_K | 2 | 13.71 GB| 16.21 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. | | chronoboros-33b.ggmlv3.q3_K_S.bin | q3_K_S | 3 | 14.06 GB| 16.56 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors | | chronoboros-33b.ggmlv3.q3_K_M.bin | q3_K_M | 3 | 15.72 GB| 18.22 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K | | chronoboros-33b.ggmlv3.q3_K_L.bin | q3_K_L | 3 | 17.28 GB| 19.78 GB | New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K | | chronoboros-33b.ggmlv3.q4_K_S.bin | q4_K_S | 4 | 18.36 GB| 20.86 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors | | chronoboros-33b.ggmlv3.q4_K_M.bin | q4_K_M | 4 | 19.62 GB| 22.12 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K | | chronoboros-33b.ggmlv3.q4_0.bin | q4_0 | 4 | 18.30 GB| 20.80 GB | Original quant method, 4-bit. | | chronoboros-33b.ggmlv3.q4_1.bin | q4_1 | 4 | 20.33 GB| 22.83 GB | Original quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. | | chronoboros-33b.ggmlv3.q5_K_S.bin | q5_K_S | 5 | 22.40 GB| 24.90 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors | | chronoboros-33b.ggmlv3.q5_K_M.bin | q5_K_M | 5 | 23.05 GB| 25.55 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K | | chronoboros-33b.ggmlv3.q5_0.bin | q5_0 | 5 | 22.37 GB| 24.87 GB | Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. | | chronoboros-33b.ggmlv3.q5_1.bin | q5_1 | 5 | 24.40 GB| 26.90 GB | Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference. | | chronoboros-33b.ggmlv3.q6_K.bin | q6_K | 6 | 26.69 GB| 29.19 GB | New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors | | chronoboros-33b.ggmlv3.q8_0.bin | q8_0 | 8 | 34.56 GB| 37.06 GB | Original quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. | **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. ## How to run in `llama.cpp` I use the following command line; adjust for your tastes and needs: ``` ./main -t 10 -ngl 32 -m chronoboros-33b.ggmlv3.q4_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: Write a story about llamas\n### Response:" ``` Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`. Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md).  ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute. Thanks to the [chirper.ai](https://chirper.ai) team! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Luke from CarbonQuill, Aemon Algiz. **Patreon special mentions**: RoA, Lone Striker, Gabriel Puliatti, Derek Yates, Randy H, Jonathan Leane, Eugene Pentland, Karl Bernard, Viktor Bowallius, senxiiz, Daniel P. Andersen, Pierre Kircher, Deep Realms, Cory Kujawski, Oscar Rangel, Fen Risland, Ajan Kanaga, LangChain4j, webtim, Nikolai Manek, Trenton Dambrowitz, Raven Klaugh, Kalila, Khalefa Al-Ahmad, Chris McCloskey, Luke @flexchar, Ai Maven, Dave, Asp the Wyvern, Sean Connelly, Imad Khwaja, Space Cruiser, Rainer Wilmers, subjectnull, Alps Aficionado, Willian Hasse, Fred von Graf, Artur Olbinski, Johann-Peter Hartmann, WelcomeToTheClub, Willem Michiel, Michael Levine, Iucharbius , Spiking Neurons AB, K, biorpg, John Villwock, Pyrater, Greatston Gnanesh, Mano Prime, Junyu Yang, Stephen Murray, John Detwiler, Luke Pendergrass, terasurfer , Pieter, zynix , Edmond Seymore, theTransient, Nathan LeClaire, vamX, Kevin Schuppel, Preetika Verma, ya boyyy, Alex , SuperWojo, Ghost , Joseph William Delisle, Matthew Berman, Talal Aujan, chris gileta, Illia Dulskyi. Thank you to all my generous patrons and donaters!  # Original model card: Henk717's Chronoboros 33B This model was the result of a 50/50 average weight merge between Airoboros-33B-1.4 and Chronos-33B. License is inhereted from all merged models, which includes the LLaMA license requiring you to own a license to use the LLaMA models. If you have such a license grant from Facebook you can request access to this model.

Knudo/distilbert-base-uncased-finetuned-cola

Knudo

2023-07-10T13:13:46Z

61

0

transformers

[ "transformers", "tf", "tensorboard", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-07-10T13:09:20Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Knudo/distilbert-base-uncased-finetuned-cola results: [] ---  # Knudo/distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1842 - Validation Loss: 0.5764 - Train Matthews Correlation: 0.5185 - Epoch: 2 ## 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: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1602, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Matthews Correlation | Epoch | |:----------:|:---------------:|:--------------------------:|:-----:| | 0.5106 | 0.4629 | 0.4797 | 0 | | 0.3111 | 0.4999 | 0.4957 | 1 | | 0.1842 | 0.5764 | 0.5185 | 2 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3

jordyvl/vit-tiny_tobacco3482_kd_MSE_test_pretrain_student

jordyvl

2023-07-10T13:01:47Z

163

0

transformers

[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-07-10T12:59:18Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: vit-tiny_tobacco3482_kd_MSE_test_pretrain_student results: [] ---  # vit-tiny_tobacco3482_kd_MSE_test_pretrain_student This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None 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: 32 - eval_batch_size: 32 - 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: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 25 | 0.6243 | 0.595 | 0.6456 | 1.9017 | 0.595 | 0.5113 | 0.3512 | 0.2202 | ### Framework versions - Transformers 4.28.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.12.0 - Tokenizers 0.12.1

datenmassiv/falcon-7b-instruct

datenmassiv

2023-07-10T13:00:34Z

13

0

transformers

[ "transformers", "pytorch", "coreml", "RefinedWebModel", "text-generation", "custom_code", "en", "dataset:tiiuae/falcon-refinedweb", "arxiv:2205.14135", "arxiv:1911.02150", "arxiv:2005.14165", "arxiv:2104.09864", "arxiv:2306.01116", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-10T13:00:33Z

--- datasets: - tiiuae/falcon-refinedweb language: - en inference: true widget: - text: Hey Falcon! Any recommendations for my holidays in Abu Dhabi? example_title: Abu Dhabi Trip - text: What's the Everett interpretation of quantum mechanics? example_title: 'Q/A: Quantum & Answers' - text: >- Give me a list of the top 10 dive sites you would recommend around the world. example_title: Diving Top 10 - text: Can you tell me more about deep-water soloing? example_title: Extreme sports - text: >- Can you write a short tweet about the Apache 2.0 release of our latest AI model, Falcon LLM? example_title: Twitter Helper - text: What are the responsabilities of a Chief Llama Officer? example_title: Trendy Jobs license: apache-2.0 duplicated_from: tiiuae/falcon-7b-instruct --- # ✨ Falcon-7B-Instruct **Falcon-7B-Instruct is a 7B parameters causal decoder-only model built by [TII](https://www.tii.ae) based on [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b) and finetuned on a mixture of chat/instruct datasets. It is made available under the Apache 2.0 license.** *Paper coming soon 😊.* 🤗 To get started with Falcon (inference, finetuning, quantization, etc.), we recommend reading [this great blogpost fron HF](https://huggingface.co/blog/falcon)! ## Why use Falcon-7B-Instruct? * **You are looking for a ready-to-use chat/instruct model based on [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b).** * **Falcon-7B is a strong base model, outperforming comparable open-source models** (e.g., [MPT-7B](https://huggingface.co/mosaicml/mpt-7b), [StableLM](https://github.com/Stability-AI/StableLM), [RedPajama](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-7B-v0.1) etc.), thanks to being trained on 1,500B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) enhanced with curated corpora. See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). * **It features an architecture optimized for inference**, with FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)) and multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)). 💬 **This is an instruct model, which may not be ideal for further finetuning.** If you are interested in building your own instruct/chat model, we recommend starting from [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b). 🔥 **Looking for an even more powerful model?** [Falcon-40B-Instruct](https://huggingface.co/tiiuae/falcon-40b-instruct) is Falcon-7B-Instruct's big brother! ```python from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model = "tiiuae/falcon-7b-instruct" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, tokenizer=tokenizer, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", ) sequences = pipeline( "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:", max_length=200, do_sample=True, top_k=10, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ``` 💥 **Falcon LLMs require PyTorch 2.0 for use with `transformers`!** For fast inference with Falcon, check-out [Text Generation Inference](https://github.com/huggingface/text-generation-inference)! Read more in this [blogpost]((https://huggingface.co/blog/falcon). You will need **at least 16GB of memory** to swiftly run inference with Falcon-7B-Instruct. # Model Card for Falcon-7B-Instruct ## Model Details ### Model Description - **Developed by:** [https://www.tii.ae](https://www.tii.ae); - **Model type:** Causal decoder-only; - **Language(s) (NLP):** English and French; - **License:** Apache 2.0; - **Finetuned from model:** [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b). ### Model Source - **Paper:** *coming soon*. ## Uses ### Direct Use Falcon-7B-Instruct has been finetuned on a mixture of instruct and chat datasets. ### Out-of-Scope Use Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful. ## Bias, Risks, and Limitations Falcon-7B-Instruct is mostly trained on English data, and will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online. ### Recommendations We recommend users of Falcon-7B-Instruct to develop guardrails and to take appropriate precautions for any production use. ## How to Get Started with the Model ```python from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model = "tiiuae/falcon-7b-instruct" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, tokenizer=tokenizer, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", ) sequences = pipeline( "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:", max_length=200, do_sample=True, top_k=10, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ``` ## Training Details ### Training Data Falcon-7B-Instruct was finetuned on a 250M tokens mixture of instruct/chat datasets. | **Data source** | **Fraction** | **Tokens** | **Description** | |--------------------|--------------|------------|-----------------------------------| | [Bai ze](https://github.com/project-baize/baize-chatbot) | 65% | 164M | chat | | [GPT4All](https://github.com/nomic-ai/gpt4all) | 25% | 62M | instruct | | [GPTeacher](https://github.com/teknium1/GPTeacher) | 5% | 11M | instruct | | [RefinedWeb-English](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) | 5% | 13M | massive web crawl | The data was tokenized with the Falcon-[7B](https://huggingface.co/tiiuae/falcon-7b)/[40B](https://huggingface.co/tiiuae/falcon-40b) tokenizer. ## Evaluation *Paper coming soon.* See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) for early results. Note that this model variant is not optimized for NLP benchmarks. ## Technical Specifications For more information about pretraining, see [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b). ### Model Architecture and Objective Falcon-7B is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token). The architecture is broadly adapted from the GPT-3 paper ([Brown et al., 2020](https://arxiv.org/abs/2005.14165)), with the following differences: * **Positionnal embeddings:** rotary ([Su et al., 2021](https://arxiv.org/abs/2104.09864)); * **Attention:** multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)) and FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)); * **Decoder-block:** parallel attention/MLP with a single layer norm. | **Hyperparameter** | **Value** | **Comment** | |--------------------|-----------|----------------------------------------| | Layers | 32 | | | `d_model` | 4544 | Increased to compensate for multiquery | | `head_dim` | 64 | Reduced to optimise for FlashAttention | | Vocabulary | 65024 | | | Sequence length | 2048 | | ### Compute Infrastructure #### Hardware Falcon-7B-Instruct was trained on AWS SageMaker, on 32 A100 40GB GPUs in P4d instances. #### Software Falcon-7B-Instruct was trained a custom distributed training codebase, Gigatron. It uses a 3D parallelism approach combined with ZeRO and high-performance Triton kernels (FlashAttention, etc.) ## Citation *Paper coming soon* 😊. In the meanwhile, you can use the following information to cite: ``` @article{falcon40b, title={{Falcon-40B}: an open large language model with state-of-the-art performance}, author={Almazrouei, Ebtesam and Alobeidli, Hamza and Alshamsi, Abdulaziz and Cappelli, Alessandro and Cojocaru, Ruxandra and Debbah, Merouane and Goffinet, Etienne and Heslow, Daniel and Launay, Julien and Malartic, Quentin and Noune, Badreddine and Pannier, Baptiste and Penedo, Guilherme}, year={2023} } ``` To learn more about the pretraining dataset, see the 📓 [RefinedWeb paper](https://arxiv.org/abs/2306.01116). ``` @article{refinedweb, title={The {R}efined{W}eb dataset for {F}alcon {LLM}: outperforming curated corpora with web data, and web data only}, author={Guilherme Penedo and Quentin Malartic and Daniel Hesslow and Ruxandra Cojocaru and Alessandro Cappelli and Hamza Alobeidli and Baptiste Pannier and Ebtesam Almazrouei and Julien Launay}, journal={arXiv preprint arXiv:2306.01116}, eprint={2306.01116}, eprinttype = {arXiv}, url={https://arxiv.org/abs/2306.01116}, year={2023} } ``` ## License Falcon-7B-Instruct is made available under the Apache 2.0 license. ## Contact [email protected]

ccattomio/Reinforce-CartPole-v1

ccattomio

2023-07-10T12:59:48Z

0

null

[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-07-10T12:59:37Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

1aurent/q-FrozenLake-v1-4x4-noSlippery

1aurent

2023-07-10T12:58:37Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-07-10T12:58:33Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="1aurent/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

bonzo1971/sesgo_genero_model

bonzo1971

2023-07-10T12:46:57Z

105

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-07-09T14:10:18Z

--- tags: - generated_from_trainer model-index: - name: sesgo_genero_model results: [] ---  # sesgo_genero_model This model is a fine-tuned version of [pysentimiento/robertuito-base-uncased](https://huggingface.co/pysentimiento/robertuito-base-uncased) on the None 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: 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 ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

GraydientPlatformAPI/model_710du

GraydientPlatformAPI

2023-07-10T12:30:48Z

29

0

diffusers

[ "diffusers", "text-to-image", "license:openrail", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-07-10T12:10:24Z

--- license: openrail library_name: diffusers pipeline_tag: text-to-image ---

Nianhua123/ppo-LunarLander-v2

Nianhua123

2023-07-10T12:24:32Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-10T12:24:15Z

--- 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: 269.69 +/- 15.15 name: mean_reward verified: false --- # **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 ... ```

PraveenJesu/openai-whisper-medium-peft-lora-v2.2.4

PraveenJesu

2023-07-10T12:24:27Z

1

0

peft

[ "peft", "region:us" ]

null

2023-07-10T12:24:25Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0

KaraAgroAI/CADI-AI

KaraAgroAI

2023-07-10T12:23:56Z

21

3

yolo

[ "yolo", "object detection", "vision", "object-detection", "en", "dataset:KaraAgroAI/CADI-AI", "license:agpl-3.0", "region:us" ]

object-detection

2023-05-17T13:54:39Z

--- license: agpl-3.0 datasets: - KaraAgroAI/CADI-AI language: - en library_name: yolo tags: - object detection - vision - yolo pipeline_tag: object-detection metrics: - mape --- ## Cashew Disease Identification with AI (CADI-AI) Model ### Model Description Object detection model trained using [YOLO v5x](https://github.com/ultralytics/yolov5/releases), a SOTA object detection algorithm. The model was pre-trained on the Cashew Disease Identification with AI (CADI-AI) train set (3788 images) at a resolution of 640x640 pixels. The CADI-AI dataset is available via [Kaggle](https://www.kaggle.com/datasets/karaagroaiprojects/cadi-ai) and [HuggingFace](https://huggingface.co/datasets/KaraAgroAI/CADI-AI). ## Intended uses You can use the raw model for object detection on cashew images. The model was initially developed to inform users whether cashew trees suffer from: - pest infection, i.e. damage to crops by insects or pests - disease, i.e. attacks on crops by microorganisms - abiotic stress caused by non-living factors (e.g. environmental factors like weather or soil conditions or the lack of mineral nutrients to the crop). KaraAgro AI developed the model for the initiatives [Market-Oriented Value Chains for Jobs & Growth in the ECOWAS Region (MOVE)](https://www.giz.de/en/worldwide/108524.html) and [FAIR Forward - Artificial Intelligence for All](https://www.bmz-digital.global/en/overview-of-initiatives/fair-forward/). Both initiatives are implemented by the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) on behalf of the German Federal Ministry for Economic Cooperation and Development (BMZ). ### How to use - Load model and perform prediction: ```bash pip install -U ultralytics ``` ```python import torch # load model model = torch.hub.load('ultralytics/yolov5', 'custom', path='CADI-AI/yolov5_0.65map_exp7_best.pt', force_reload=True) # Images img = ['/path/to/your/image.jpg']# batch of images # set model parameters # set Non-Maximum-Suppression(NMS) threshold to define # minimum confidence score that a bounding box must have in order to be kept. model.conf = 0.20 # NMS confidence threshold # perform inference results = model(img, size=640) # Results results.print() results.xyxy[0] # img1 predictions (tensor) results.pandas().xyxy[0] # img1 predictions (pandas) # parse results predictions = results.pred[0] boxes = predictions[:, :4] # x1, y1, x2, y2 scores = predictions[:, 4] categories = predictions[:, 5] # show detection bounding boxes on image results.show() # save results into "results/" folder results.save(save_dir='results/') ``` - Finetune the model on your custom dataset: ```bash yolov5 train --data data.yaml --img 640 --batch 16 --weights KaraAgroAI/CADI-AI --epochs 10 ``` ### Model performance | Class | Precision | Recall | mAP@50 | mAP@50-95 | | --- | --- | --- | --- | --- | | all | 0.663 | 0.632 | 0.648 | 0.291 | | insect | 0.794 | 0.811 | 0.815 | 0.39 | | abiotic | 0.682 | 0.514 | 0.542 | 0.237 | | disease | 0.594 | 0.571 | 0.588 | 0.248 | ### Limitations of the Model The model has a few limitations that affect its performance in distinguishing between the disease class and the abiotic class. The primary challenge lies in the similarity between these two classes within a typical farm setting. The model may encounter difficulties in accurately differentiating between them due to their overlapping characteristics. This limitation is an inherent challenge in the dataset and can impact the model's accuracy when classifying these classes. However, it is worth noting that the model exhibits strong performance when it comes to the insect class. This is attributed to the distinct characteristics of insect class, which make them easier to identify and classify accurately. ### Demo [CADI-AI Spaces demonstration](https://huggingface.co/spaces/KaraAgroAI/CADI-AI) ### Project Repo If you want to know how the model and dataset has been used further for the GIZ-funded activity, please have a look at: - The [GitHub repository](https://github.com/karaagro/cadi-ai) for the CADI AI desktop application ### Example prediction <div align="center"> <img width="640" alt="KaraAgroAI/CADI-AI" src="https://huggingface.co/KaraAgroAI/CADI-AI/resolve/main/sample.jpg"> </div>

Mavila/First_DRL

Mavila

2023-07-10T12:06:41Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-10T12:06:19Z

--- 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: 261.12 +/- 13.54 name: mean_reward verified: false --- # **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 ... ```

jordyvl/dit-small_rvl_cdip_100_examples_per_class_kd_MSE_lr_fix

jordyvl

2023-07-10T12:04:08Z

161

0

transformers

[ "transformers", "pytorch", "beit", "image-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-07-10T11:10:29Z

--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: dit-small_rvl_cdip_100_examples_per_class_kd_MSE_lr_fix results: [] ---  # dit-small_rvl_cdip_100_examples_per_class_kd_MSE_lr_fix This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8796 - Accuracy: 0.26 - Brier Loss: 0.8768 - Nll: 6.0962 - F1 Micro: 0.26 - F1 Macro: 0.2480 - Ece: 0.2002 - Aurc: 0.5815 ## 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: 128 - eval_batch_size: 128 - 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: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:-------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 1.5365 | 0.065 | 0.9398 | 10.2864 | 0.065 | 0.0116 | 0.1183 | 0.9536 | | No log | 2.0 | 14 | 1.5332 | 0.06 | 0.9374 | 9.8468 | 0.06 | 0.0269 | 0.1067 | 0.9096 | | No log | 3.0 | 21 | 1.5119 | 0.085 | 0.9352 | 9.1495 | 0.085 | 0.0355 | 0.1135 | 0.8759 | | No log | 4.0 | 28 | 1.5040 | 0.0825 | 0.9333 | 8.6549 | 0.0825 | 0.0439 | 0.1181 | 0.8618 | | No log | 5.0 | 35 | 1.5021 | 0.1 | 0.9301 | 8.9643 | 0.1000 | 0.0558 | 0.1318 | 0.8030 | | No log | 6.0 | 42 | 1.4885 | 0.1 | 0.9276 | 7.8684 | 0.1000 | 0.0505 | 0.1205 | 0.8190 | | No log | 7.0 | 49 | 1.4882 | 0.0975 | 0.9254 | 9.4095 | 0.0975 | 0.0584 | 0.1220 | 0.7847 | | No log | 8.0 | 56 | 1.4909 | 0.1275 | 0.9227 | 9.4274 | 0.1275 | 0.0827 | 0.1335 | 0.7445 | | No log | 9.0 | 63 | 1.4837 | 0.115 | 0.9217 | 10.2918 | 0.115 | 0.0546 | 0.1366 | 0.7932 | | No log | 10.0 | 70 | 1.4857 | 0.1125 | 0.9186 | 9.5039 | 0.1125 | 0.0510 | 0.1277 | 0.7749 | | No log | 11.0 | 77 | 1.4804 | 0.1125 | 0.9183 | 8.5178 | 0.1125 | 0.0515 | 0.1315 | 0.7831 | | No log | 12.0 | 84 | 1.4701 | 0.11 | 0.9177 | 8.2398 | 0.11 | 0.0655 | 0.1310 | 0.7754 | | No log | 13.0 | 91 | 1.4721 | 0.16 | 0.9160 | 7.2379 | 0.16 | 0.1155 | 0.1462 | 0.7370 | | No log | 14.0 | 98 | 1.4717 | 0.11 | 0.9159 | 8.1355 | 0.11 | 0.0633 | 0.1221 | 0.7579 | | No log | 15.0 | 105 | 1.4739 | 0.1325 | 0.9138 | 7.4037 | 0.1325 | 0.0790 | 0.1419 | 0.7358 | | No log | 16.0 | 112 | 1.4657 | 0.1425 | 0.9135 | 7.8063 | 0.1425 | 0.0821 | 0.1285 | 0.7269 | | No log | 17.0 | 119 | 1.4632 | 0.1375 | 0.9112 | 7.8852 | 0.1375 | 0.0948 | 0.1389 | 0.7342 | | No log | 18.0 | 126 | 1.4769 | 0.15 | 0.9081 | 8.5375 | 0.15 | 0.0894 | 0.1399 | 0.7113 | | No log | 19.0 | 133 | 1.4547 | 0.1775 | 0.9045 | 6.4114 | 0.1775 | 0.1174 | 0.1507 | 0.7007 | | No log | 20.0 | 140 | 1.4470 | 0.1725 | 0.9031 | 8.1696 | 0.1725 | 0.1246 | 0.1464 | 0.7079 | | No log | 21.0 | 147 | 1.4615 | 0.19 | 0.9021 | 6.0696 | 0.19 | 0.1390 | 0.1646 | 0.7023 | | No log | 22.0 | 154 | 1.4588 | 0.2 | 0.8996 | 6.0038 | 0.2000 | 0.1384 | 0.1628 | 0.6821 | | No log | 23.0 | 161 | 1.4646 | 0.1525 | 0.8988 | 7.0678 | 0.1525 | 0.1075 | 0.1458 | 0.7000 | | No log | 24.0 | 168 | 1.4491 | 0.2125 | 0.8933 | 5.9276 | 0.2125 | 0.1503 | 0.1533 | 0.6457 | | No log | 25.0 | 175 | 1.4526 | 0.205 | 0.8916 | 7.6108 | 0.205 | 0.1479 | 0.1603 | 0.6676 | | No log | 26.0 | 182 | 1.4510 | 0.17 | 0.8910 | 5.6337 | 0.17 | 0.1333 | 0.1396 | 0.6868 | | No log | 27.0 | 189 | 1.4567 | 0.19 | 0.8850 | 5.2038 | 0.19 | 0.1380 | 0.1637 | 0.6547 | | No log | 28.0 | 196 | 1.4570 | 0.2225 | 0.8846 | 6.5368 | 0.2225 | 0.1840 | 0.1701 | 0.6554 | | No log | 29.0 | 203 | 1.4701 | 0.2075 | 0.8820 | 5.0057 | 0.2075 | 0.1663 | 0.1719 | 0.6598 | | No log | 30.0 | 210 | 1.4693 | 0.2225 | 0.8755 | 7.4456 | 0.2225 | 0.1729 | 0.1626 | 0.6355 | | No log | 31.0 | 217 | 1.4670 | 0.23 | 0.8787 | 5.8938 | 0.23 | 0.1904 | 0.1717 | 0.6424 | | No log | 32.0 | 224 | 1.4540 | 0.2275 | 0.8756 | 6.6513 | 0.2275 | 0.1673 | 0.1676 | 0.6306 | | No log | 33.0 | 231 | 1.4641 | 0.2275 | 0.8649 | 5.5689 | 0.2275 | 0.1751 | 0.1746 | 0.6138 | | No log | 34.0 | 238 | 1.4710 | 0.2425 | 0.8640 | 7.0556 | 0.2425 | 0.1957 | 0.1809 | 0.6048 | | No log | 35.0 | 245 | 1.4685 | 0.23 | 0.8632 | 5.5735 | 0.23 | 0.1940 | 0.1609 | 0.6188 | | No log | 36.0 | 252 | 1.4665 | 0.2375 | 0.8592 | 5.8835 | 0.2375 | 0.1952 | 0.1727 | 0.6050 | | No log | 37.0 | 259 | 1.4668 | 0.235 | 0.8540 | 5.3502 | 0.235 | 0.1966 | 0.1746 | 0.6056 | | No log | 38.0 | 266 | 1.4855 | 0.27 | 0.8510 | 5.3781 | 0.27 | 0.2124 | 0.1692 | 0.5825 | | No log | 39.0 | 273 | 1.5279 | 0.265 | 0.8562 | 6.2426 | 0.265 | 0.2126 | 0.1772 | 0.5831 | | No log | 40.0 | 280 | 1.5433 | 0.2425 | 0.8551 | 5.9574 | 0.2425 | 0.1867 | 0.1499 | 0.5874 | | No log | 41.0 | 287 | 1.5955 | 0.2525 | 0.8597 | 6.1628 | 0.2525 | 0.2024 | 0.1479 | 0.5891 | | No log | 42.0 | 294 | 1.5528 | 0.2475 | 0.8541 | 6.3624 | 0.2475 | 0.1908 | 0.1566 | 0.5735 | | No log | 43.0 | 301 | 1.5858 | 0.2675 | 0.8504 | 6.1261 | 0.2675 | 0.2174 | 0.1706 | 0.5674 | | No log | 44.0 | 308 | 1.6013 | 0.2725 | 0.8496 | 5.8409 | 0.2725 | 0.2463 | 0.1846 | 0.5807 | | No log | 45.0 | 315 | 1.5632 | 0.2625 | 0.8472 | 5.9669 | 0.2625 | 0.2307 | 0.1689 | 0.5689 | | No log | 46.0 | 322 | 1.6520 | 0.2675 | 0.8509 | 5.8544 | 0.2675 | 0.2325 | 0.1779 | 0.5622 | | No log | 47.0 | 329 | 1.6135 | 0.2625 | 0.8476 | 5.5208 | 0.2625 | 0.2504 | 0.1565 | 0.5759 | | No log | 48.0 | 336 | 1.6565 | 0.275 | 0.8466 | 5.9254 | 0.275 | 0.2527 | 0.2026 | 0.5616 | | No log | 49.0 | 343 | 1.6807 | 0.2625 | 0.8531 | 6.1297 | 0.2625 | 0.2259 | 0.1813 | 0.5664 | | No log | 50.0 | 350 | 1.7266 | 0.255 | 0.8560 | 6.0828 | 0.255 | 0.2315 | 0.1817 | 0.5735 | | No log | 51.0 | 357 | 1.7038 | 0.2525 | 0.8579 | 5.6442 | 0.2525 | 0.2405 | 0.1861 | 0.5828 | | No log | 52.0 | 364 | 1.7954 | 0.255 | 0.8583 | 5.7016 | 0.255 | 0.2227 | 0.1722 | 0.5725 | | No log | 53.0 | 371 | 1.7567 | 0.275 | 0.8557 | 6.1586 | 0.275 | 0.2523 | 0.1577 | 0.5619 | | No log | 54.0 | 378 | 1.7589 | 0.2525 | 0.8565 | 5.3969 | 0.2525 | 0.2325 | 0.1840 | 0.5661 | | No log | 55.0 | 385 | 1.7778 | 0.265 | 0.8569 | 5.8559 | 0.265 | 0.2447 | 0.1835 | 0.5640 | | No log | 56.0 | 392 | 1.8044 | 0.275 | 0.8592 | 5.9942 | 0.275 | 0.2517 | 0.1783 | 0.5627 | | No log | 57.0 | 399 | 1.8327 | 0.2625 | 0.8628 | 6.0224 | 0.2625 | 0.2333 | 0.1801 | 0.5560 | | No log | 58.0 | 406 | 1.8184 | 0.25 | 0.8609 | 6.0769 | 0.25 | 0.2333 | 0.1941 | 0.5718 | | No log | 59.0 | 413 | 1.8318 | 0.2575 | 0.8639 | 5.9454 | 0.2575 | 0.2364 | 0.1965 | 0.5743 | | No log | 60.0 | 420 | 1.8081 | 0.2525 | 0.8641 | 6.0119 | 0.2525 | 0.2380 | 0.1818 | 0.5755 | | No log | 61.0 | 427 | 1.8405 | 0.2625 | 0.8775 | 6.2129 | 0.2625 | 0.2474 | 0.1767 | 0.5908 | | No log | 62.0 | 434 | 1.9012 | 0.2625 | 0.8728 | 6.1015 | 0.2625 | 0.2373 | 0.1881 | 0.5716 | | No log | 63.0 | 441 | 1.8500 | 0.26 | 0.8728 | 6.3885 | 0.26 | 0.2414 | 0.1933 | 0.5809 | | No log | 64.0 | 448 | 1.8771 | 0.2675 | 0.8733 | 6.2730 | 0.2675 | 0.2553 | 0.2035 | 0.5800 | | No log | 65.0 | 455 | 1.8744 | 0.2575 | 0.8677 | 5.9805 | 0.2575 | 0.2392 | 0.1918 | 0.5663 | | No log | 66.0 | 462 | 1.8366 | 0.255 | 0.8694 | 6.0073 | 0.255 | 0.2403 | 0.2048 | 0.5807 | | No log | 67.0 | 469 | 1.8758 | 0.2575 | 0.8743 | 6.1015 | 0.2575 | 0.2381 | 0.2071 | 0.5825 | | No log | 68.0 | 476 | 1.8796 | 0.2675 | 0.8711 | 5.9457 | 0.2675 | 0.2470 | 0.2100 | 0.5737 | | No log | 69.0 | 483 | 1.8635 | 0.2675 | 0.8721 | 5.9312 | 0.2675 | 0.2493 | 0.1788 | 0.5751 | | No log | 70.0 | 490 | 1.8801 | 0.2625 | 0.8710 | 5.9629 | 0.2625 | 0.2467 | 0.1974 | 0.5721 | | No log | 71.0 | 497 | 1.8936 | 0.26 | 0.8791 | 6.0358 | 0.26 | 0.2481 | 0.1922 | 0.5844 | | 0.9216 | 72.0 | 504 | 1.8736 | 0.275 | 0.8715 | 6.0493 | 0.275 | 0.2569 | 0.2099 | 0.5710 | | 0.9216 | 73.0 | 511 | 1.8784 | 0.2525 | 0.8760 | 6.1441 | 0.2525 | 0.2401 | 0.1978 | 0.5849 | | 0.9216 | 74.0 | 518 | 1.8843 | 0.2725 | 0.8763 | 6.1948 | 0.2725 | 0.2533 | 0.2007 | 0.5801 | | 0.9216 | 75.0 | 525 | 1.8785 | 0.2675 | 0.8784 | 5.9868 | 0.2675 | 0.2578 | 0.1975 | 0.5851 | | 0.9216 | 76.0 | 532 | 1.8812 | 0.275 | 0.8725 | 5.9367 | 0.275 | 0.2594 | 0.2037 | 0.5744 | | 0.9216 | 77.0 | 539 | 1.8956 | 0.27 | 0.8746 | 5.9038 | 0.27 | 0.2541 | 0.1816 | 0.5738 | | 0.9216 | 78.0 | 546 | 1.8897 | 0.265 | 0.8802 | 5.9763 | 0.265 | 0.2493 | 0.2098 | 0.5866 | | 0.9216 | 79.0 | 553 | 1.8728 | 0.275 | 0.8752 | 6.0806 | 0.275 | 0.2623 | 0.1874 | 0.5794 | | 0.9216 | 80.0 | 560 | 1.8887 | 0.2725 | 0.8759 | 6.2762 | 0.2725 | 0.2520 | 0.2005 | 0.5768 | | 0.9216 | 81.0 | 567 | 1.8987 | 0.2725 | 0.8787 | 6.2444 | 0.2725 | 0.2587 | 0.2183 | 0.5773 | | 0.9216 | 82.0 | 574 | 1.8759 | 0.2625 | 0.8773 | 6.1643 | 0.2625 | 0.2541 | 0.1922 | 0.5805 | | 0.9216 | 83.0 | 581 | 1.8766 | 0.27 | 0.8748 | 6.0036 | 0.27 | 0.2554 | 0.1784 | 0.5762 | | 0.9216 | 84.0 | 588 | 1.8809 | 0.2625 | 0.8764 | 6.0488 | 0.2625 | 0.2469 | 0.2030 | 0.5833 | | 0.9216 | 85.0 | 595 | 1.8982 | 0.26 | 0.8775 | 6.0747 | 0.26 | 0.2453 | 0.1998 | 0.5851 | | 0.9216 | 86.0 | 602 | 1.8912 | 0.27 | 0.8798 | 6.1894 | 0.27 | 0.2566 | 0.1938 | 0.5839 | | 0.9216 | 87.0 | 609 | 1.8847 | 0.2775 | 0.8769 | 6.2744 | 0.2775 | 0.2643 | 0.2019 | 0.5775 | | 0.9216 | 88.0 | 616 | 1.8734 | 0.265 | 0.8741 | 6.1928 | 0.265 | 0.2526 | 0.1763 | 0.5820 | | 0.9216 | 89.0 | 623 | 1.8760 | 0.2725 | 0.8768 | 6.0274 | 0.2725 | 0.2620 | 0.2039 | 0.5792 | | 0.9216 | 90.0 | 630 | 1.8860 | 0.265 | 0.8771 | 6.0912 | 0.265 | 0.2518 | 0.1924 | 0.5810 | | 0.9216 | 91.0 | 637 | 1.8865 | 0.2625 | 0.8750 | 6.2350 | 0.2625 | 0.2476 | 0.1844 | 0.5791 | | 0.9216 | 92.0 | 644 | 1.8815 | 0.2725 | 0.8733 | 6.0962 | 0.2725 | 0.2563 | 0.2013 | 0.5721 | | 0.9216 | 93.0 | 651 | 1.8794 | 0.27 | 0.8756 | 6.2535 | 0.27 | 0.2562 | 0.2028 | 0.5764 | | 0.9216 | 94.0 | 658 | 1.8835 | 0.2675 | 0.8769 | 6.2039 | 0.2675 | 0.2562 | 0.1928 | 0.5773 | | 0.9216 | 95.0 | 665 | 1.8904 | 0.27 | 0.8786 | 6.1504 | 0.27 | 0.2543 | 0.2034 | 0.5768 | | 0.9216 | 96.0 | 672 | 1.8911 | 0.26 | 0.8788 | 6.1527 | 0.26 | 0.2465 | 0.2025 | 0.5829 | | 0.9216 | 97.0 | 679 | 1.8871 | 0.265 | 0.8776 | 6.0994 | 0.265 | 0.2519 | 0.2126 | 0.5794 | | 0.9216 | 98.0 | 686 | 1.8825 | 0.265 | 0.8769 | 6.1564 | 0.265 | 0.2516 | 0.1987 | 0.5776 | | 0.9216 | 99.0 | 693 | 1.8803 | 0.2675 | 0.8766 | 6.1183 | 0.2675 | 0.2561 | 0.2095 | 0.5798 | | 0.9216 | 100.0 | 700 | 1.8796 | 0.26 | 0.8768 | 6.0962 | 0.26 | 0.2480 | 0.2002 | 0.5815 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

HeinrichWirth/taxi

HeinrichWirth

2023-07-10T11:44:14Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-07-10T11:43:46Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="HeinrichWirth/taxi", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

tyavika/LR1E4-BS16-Distil-CNN512LSTM256NoBi

tyavika

2023-07-10T11:29:13Z

76

0

transformers

[ "transformers", "pytorch", "distilbert", "question-answering", "generated_from_trainer", "endpoints_compatible", "region:us" ]

question-answering

2023-07-09T20:24:08Z

--- tags: - generated_from_trainer model-index: - name: LR1E4-BS16-Distil-CNN512LSTM256NoBi results: [] ---  # LR1E4-BS16-Distil-CNN512LSTM256NoBi This model is a fine-tuned version of [tyavika/LR1E4-BS16-Distil-CNN512LSTM256NoBi](https://huggingface.co/tyavika/LR1E4-BS16-Distil-CNN512LSTM256NoBi) on the None 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: 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: 10 ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

Junr-syl/tweet_sentiments_analysis

Junr-syl

2023-07-10T11:21:39Z

162

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-07-07T09:19:12Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: tweet_sentiments_analysis results: [] ---  # tweet_sentiments_analysis 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: - eval_loss: 0.3953 - eval_accuracy: 0.8660 - eval_runtime: 254.1512 - eval_samples_per_second: 31.473 - eval_steps_per_second: 3.935 - step: 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: 5e-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: 1000 ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

jordyvl/dit-base_tobacco_small_student

jordyvl

2023-07-10T10:58:25Z

161

0

transformers

[ "transformers", "pytorch", "tensorboard", "beit", "image-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-07-10T10:07:51Z

--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: dit-base_tobacco_small_student results: [] ---  # dit-base_tobacco_small_student This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.3305 - Accuracy: 0.435 - Brier Loss: 1.0472 - Nll: 10.3327 - F1 Micro: 0.435 - F1 Macro: 0.4299 - Ece: 0.5115 - Aurc: 0.4245 ## 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: 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: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:-------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 50 | 2.1780 | 0.16 | 0.8745 | 11.2696 | 0.16 | 0.0323 | 0.2326 | 0.8208 | | No log | 2.0 | 100 | 2.1761 | 0.19 | 0.8727 | 10.5065 | 0.19 | 0.0548 | 0.2712 | 0.7980 | | No log | 3.0 | 150 | 2.1426 | 0.16 | 0.8689 | 8.8915 | 0.16 | 0.0451 | 0.2697 | 0.6322 | | No log | 4.0 | 200 | 2.0668 | 0.225 | 0.8434 | 9.6036 | 0.225 | 0.1219 | 0.2680 | 0.6623 | | No log | 5.0 | 250 | 2.0633 | 0.21 | 0.8447 | 5.7679 | 0.2100 | 0.1401 | 0.2733 | 0.5765 | | No log | 6.0 | 300 | 2.0030 | 0.22 | 0.8351 | 7.1501 | 0.22 | 0.1132 | 0.3000 | 0.6750 | | No log | 7.0 | 350 | 1.9273 | 0.32 | 0.8243 | 6.2911 | 0.32 | 0.2612 | 0.2822 | 0.6549 | | No log | 8.0 | 400 | 1.7954 | 0.365 | 0.7742 | 4.2641 | 0.3650 | 0.2647 | 0.2630 | 0.5031 | | No log | 9.0 | 450 | 1.8070 | 0.36 | 0.7720 | 4.9274 | 0.36 | 0.2914 | 0.2601 | 0.4871 | | 1.9795 | 10.0 | 500 | 1.7838 | 0.34 | 0.7857 | 3.3860 | 0.34 | 0.2387 | 0.2902 | 0.5057 | | 1.9795 | 11.0 | 550 | 1.7214 | 0.395 | 0.7404 | 4.1630 | 0.395 | 0.2995 | 0.2922 | 0.4210 | | 1.9795 | 12.0 | 600 | 1.6834 | 0.445 | 0.7284 | 3.7081 | 0.445 | 0.3444 | 0.2700 | 0.3914 | | 1.9795 | 13.0 | 650 | 1.6992 | 0.38 | 0.7641 | 4.1246 | 0.38 | 0.3045 | 0.3375 | 0.4155 | | 1.9795 | 14.0 | 700 | 1.8695 | 0.395 | 0.7711 | 5.6899 | 0.395 | 0.3432 | 0.3224 | 0.4425 | | 1.9795 | 15.0 | 750 | 1.8757 | 0.38 | 0.7939 | 5.1099 | 0.38 | 0.3879 | 0.3102 | 0.4313 | | 1.9795 | 16.0 | 800 | 2.0457 | 0.405 | 0.8184 | 5.6034 | 0.405 | 0.3957 | 0.3256 | 0.4414 | | 1.9795 | 17.0 | 850 | 2.2243 | 0.395 | 0.8653 | 7.7124 | 0.395 | 0.3567 | 0.3887 | 0.3997 | | 1.9795 | 18.0 | 900 | 1.9309 | 0.45 | 0.7794 | 5.2698 | 0.45 | 0.3763 | 0.3626 | 0.3767 | | 1.9795 | 19.0 | 950 | 2.2285 | 0.415 | 0.8319 | 6.7127 | 0.415 | 0.4153 | 0.3667 | 0.3942 | | 0.6717 | 20.0 | 1000 | 2.3745 | 0.445 | 0.8643 | 7.4432 | 0.445 | 0.4290 | 0.3859 | 0.4046 | | 0.6717 | 21.0 | 1050 | 2.5389 | 0.41 | 0.9148 | 7.6865 | 0.41 | 0.3994 | 0.4351 | 0.4054 | | 0.6717 | 22.0 | 1100 | 2.5537 | 0.465 | 0.8500 | 8.1266 | 0.465 | 0.4623 | 0.4070 | 0.3900 | | 0.6717 | 23.0 | 1150 | 2.8355 | 0.42 | 0.9426 | 8.8542 | 0.4200 | 0.3930 | 0.4508 | 0.4201 | | 0.6717 | 24.0 | 1200 | 2.8575 | 0.4 | 0.9962 | 7.6428 | 0.4000 | 0.3502 | 0.4994 | 0.4119 | | 0.6717 | 25.0 | 1250 | 2.8704 | 0.445 | 0.9418 | 9.2600 | 0.445 | 0.4570 | 0.4309 | 0.4021 | | 0.6717 | 26.0 | 1300 | 3.4702 | 0.43 | 0.9641 | 12.1621 | 0.4300 | 0.3977 | 0.4590 | 0.3597 | | 0.6717 | 27.0 | 1350 | 3.1484 | 0.475 | 0.9518 | 8.1474 | 0.4750 | 0.4641 | 0.4809 | 0.4088 | | 0.6717 | 28.0 | 1400 | 3.2299 | 0.455 | 0.9673 | 9.6161 | 0.455 | 0.4205 | 0.4711 | 0.3806 | | 0.6717 | 29.0 | 1450 | 3.4968 | 0.425 | 1.0136 | 10.5614 | 0.425 | 0.3992 | 0.4743 | 0.3773 | | 0.0268 | 30.0 | 1500 | 3.1340 | 0.46 | 0.9443 | 8.5023 | 0.46 | 0.4296 | 0.4557 | 0.3735 | | 0.0268 | 31.0 | 1550 | 3.4297 | 0.435 | 1.0058 | 8.2428 | 0.435 | 0.3979 | 0.4967 | 0.3848 | | 0.0268 | 32.0 | 1600 | 3.6922 | 0.4 | 1.0488 | 10.8019 | 0.4000 | 0.3880 | 0.5223 | 0.4017 | | 0.0268 | 33.0 | 1650 | 3.6009 | 0.445 | 0.9964 | 10.1007 | 0.445 | 0.4204 | 0.4924 | 0.3981 | | 0.0268 | 34.0 | 1700 | 3.6678 | 0.425 | 1.0494 | 9.1369 | 0.425 | 0.3896 | 0.5159 | 0.4192 | | 0.0268 | 35.0 | 1750 | 3.5743 | 0.45 | 0.9953 | 9.5996 | 0.45 | 0.4182 | 0.4934 | 0.4030 | | 0.0268 | 36.0 | 1800 | 3.5551 | 0.465 | 0.9877 | 9.6080 | 0.465 | 0.4221 | 0.5033 | 0.3977 | | 0.0268 | 37.0 | 1850 | 3.7424 | 0.435 | 1.0191 | 9.9258 | 0.435 | 0.4292 | 0.4955 | 0.4120 | | 0.0268 | 38.0 | 1900 | 3.7093 | 0.45 | 1.0051 | 9.7038 | 0.45 | 0.4033 | 0.4966 | 0.3857 | | 0.0268 | 39.0 | 1950 | 3.7240 | 0.45 | 1.0076 | 9.8462 | 0.45 | 0.4027 | 0.4953 | 0.3962 | | 0.0022 | 40.0 | 2000 | 3.7503 | 0.455 | 1.0090 | 9.9967 | 0.455 | 0.4076 | 0.5056 | 0.3968 | | 0.0022 | 41.0 | 2050 | 3.5545 | 0.44 | 1.0007 | 8.7616 | 0.44 | 0.4285 | 0.4894 | 0.4008 | | 0.0022 | 42.0 | 2100 | 3.7452 | 0.435 | 1.0142 | 9.4376 | 0.435 | 0.4135 | 0.5032 | 0.4022 | | 0.0022 | 43.0 | 2150 | 3.5980 | 0.47 | 0.9532 | 8.2333 | 0.47 | 0.4441 | 0.4650 | 0.4113 | | 0.0022 | 44.0 | 2200 | 3.7055 | 0.45 | 0.9946 | 9.0121 | 0.45 | 0.4327 | 0.4817 | 0.3688 | | 0.0022 | 45.0 | 2250 | 3.8500 | 0.435 | 1.0161 | 9.2035 | 0.435 | 0.4164 | 0.5128 | 0.3723 | | 0.0022 | 46.0 | 2300 | 3.8806 | 0.435 | 1.0261 | 10.7033 | 0.435 | 0.4323 | 0.5008 | 0.3812 | | 0.0022 | 47.0 | 2350 | 3.8114 | 0.455 | 1.0128 | 9.6784 | 0.455 | 0.4236 | 0.5025 | 0.3873 | | 0.0022 | 48.0 | 2400 | 3.8743 | 0.435 | 1.0294 | 8.7193 | 0.435 | 0.3734 | 0.5109 | 0.3783 | | 0.0022 | 49.0 | 2450 | 3.9281 | 0.43 | 1.0414 | 9.9489 | 0.4300 | 0.4296 | 0.5047 | 0.4049 | | 0.0047 | 50.0 | 2500 | 3.7824 | 0.45 | 0.9956 | 10.7814 | 0.45 | 0.4467 | 0.4975 | 0.3949 | | 0.0047 | 51.0 | 2550 | 4.0089 | 0.475 | 0.9668 | 11.9005 | 0.4750 | 0.4253 | 0.4637 | 0.4501 | | 0.0047 | 52.0 | 2600 | 3.7248 | 0.43 | 0.9909 | 10.6449 | 0.4300 | 0.4064 | 0.4750 | 0.4023 | | 0.0047 | 53.0 | 2650 | 3.7911 | 0.415 | 1.0491 | 9.1188 | 0.415 | 0.3608 | 0.5130 | 0.4173 | | 0.0047 | 54.0 | 2700 | 3.6925 | 0.44 | 1.0000 | 8.9655 | 0.44 | 0.3970 | 0.4826 | 0.4168 | | 0.0047 | 55.0 | 2750 | 3.6214 | 0.46 | 0.9590 | 9.5422 | 0.46 | 0.4440 | 0.4636 | 0.3829 | | 0.0047 | 56.0 | 2800 | 4.3545 | 0.405 | 1.0811 | 10.6531 | 0.405 | 0.4090 | 0.5439 | 0.4533 | | 0.0047 | 57.0 | 2850 | 3.6835 | 0.46 | 0.9717 | 8.2408 | 0.46 | 0.4367 | 0.4950 | 0.4118 | | 0.0047 | 58.0 | 2900 | 4.0080 | 0.465 | 1.0011 | 9.3764 | 0.465 | 0.4579 | 0.4927 | 0.4234 | | 0.0047 | 59.0 | 2950 | 4.0141 | 0.45 | 1.0014 | 9.7100 | 0.45 | 0.4443 | 0.4987 | 0.4220 | | 0.0118 | 60.0 | 3000 | 3.7963 | 0.43 | 1.0135 | 9.4040 | 0.4300 | 0.4007 | 0.5007 | 0.3979 | | 0.0118 | 61.0 | 3050 | 4.0609 | 0.43 | 1.0426 | 9.3533 | 0.4300 | 0.3825 | 0.5266 | 0.4285 | | 0.0118 | 62.0 | 3100 | 4.0150 | 0.47 | 1.0002 | 9.3307 | 0.47 | 0.4490 | 0.5030 | 0.4052 | | 0.0118 | 63.0 | 3150 | 3.7982 | 0.47 | 0.9660 | 8.5060 | 0.47 | 0.4581 | 0.4716 | 0.3988 | | 0.0118 | 64.0 | 3200 | 4.3553 | 0.44 | 1.0428 | 10.3840 | 0.44 | 0.4218 | 0.5163 | 0.4312 | | 0.0118 | 65.0 | 3250 | 3.7142 | 0.44 | 0.9900 | 8.5049 | 0.44 | 0.4298 | 0.4849 | 0.3735 | | 0.0118 | 66.0 | 3300 | 3.7411 | 0.47 | 0.9661 | 8.1935 | 0.47 | 0.4497 | 0.4789 | 0.3812 | | 0.0118 | 67.0 | 3350 | 3.7858 | 0.49 | 0.9574 | 8.8397 | 0.49 | 0.4799 | 0.4616 | 0.3895 | | 0.0118 | 68.0 | 3400 | 3.7927 | 0.495 | 0.9459 | 8.6915 | 0.495 | 0.4870 | 0.4577 | 0.3883 | | 0.0118 | 69.0 | 3450 | 3.8348 | 0.5 | 0.9454 | 8.8298 | 0.5 | 0.4889 | 0.4715 | 0.3891 | | 0.0004 | 70.0 | 3500 | 3.8551 | 0.48 | 0.9500 | 8.9827 | 0.48 | 0.4755 | 0.4691 | 0.3913 | | 0.0004 | 71.0 | 3550 | 3.8432 | 0.48 | 0.9622 | 9.1404 | 0.48 | 0.4691 | 0.4690 | 0.3885 | | 0.0004 | 72.0 | 3600 | 3.8594 | 0.48 | 0.9617 | 8.8182 | 0.48 | 0.4691 | 0.4805 | 0.3854 | | 0.0004 | 73.0 | 3650 | 3.8855 | 0.485 | 0.9622 | 8.8248 | 0.485 | 0.4760 | 0.4809 | 0.3881 | | 0.0004 | 74.0 | 3700 | 3.8996 | 0.49 | 0.9610 | 8.9750 | 0.49 | 0.4818 | 0.4634 | 0.3892 | | 0.0004 | 75.0 | 3750 | 3.9921 | 0.475 | 0.9642 | 9.5409 | 0.4750 | 0.4597 | 0.4666 | 0.4185 | | 0.0004 | 76.0 | 3800 | 4.1128 | 0.43 | 1.0429 | 9.9966 | 0.4300 | 0.3844 | 0.5187 | 0.4056 | | 0.0004 | 77.0 | 3850 | 4.0783 | 0.44 | 1.0172 | 9.3016 | 0.44 | 0.4205 | 0.5051 | 0.3988 | | 0.0004 | 78.0 | 3900 | 4.0804 | 0.445 | 1.0254 | 8.9753 | 0.445 | 0.4246 | 0.5089 | 0.3982 | | 0.0004 | 79.0 | 3950 | 4.0892 | 0.445 | 1.0269 | 8.8290 | 0.445 | 0.4246 | 0.5069 | 0.4000 | | 0.0002 | 80.0 | 4000 | 4.1013 | 0.445 | 1.0258 | 9.1363 | 0.445 | 0.4246 | 0.5129 | 0.4033 | | 0.0002 | 81.0 | 4050 | 4.0985 | 0.44 | 1.0287 | 9.1459 | 0.44 | 0.4213 | 0.5074 | 0.4054 | | 0.0002 | 82.0 | 4100 | 4.1029 | 0.44 | 1.0263 | 9.3107 | 0.44 | 0.4211 | 0.5125 | 0.4066 | | 0.0002 | 83.0 | 4150 | 4.1075 | 0.44 | 1.0248 | 9.4604 | 0.44 | 0.4224 | 0.5164 | 0.4061 | | 0.0002 | 84.0 | 4200 | 4.1087 | 0.44 | 1.0225 | 9.7739 | 0.44 | 0.4221 | 0.5090 | 0.4055 | | 0.0002 | 85.0 | 4250 | 4.1248 | 0.44 | 1.0262 | 9.7747 | 0.44 | 0.4259 | 0.5032 | 0.4065 | | 0.0002 | 86.0 | 4300 | 4.1527 | 0.445 | 1.0263 | 9.4647 | 0.445 | 0.4299 | 0.5128 | 0.4066 | | 0.0002 | 87.0 | 4350 | 4.0529 | 0.475 | 0.9810 | 9.1439 | 0.4750 | 0.4488 | 0.4910 | 0.3938 | | 0.0002 | 88.0 | 4400 | 4.1405 | 0.455 | 1.0091 | 9.5149 | 0.455 | 0.4230 | 0.4966 | 0.4147 | | 0.0002 | 89.0 | 4450 | 4.3483 | 0.41 | 1.0724 | 9.8421 | 0.41 | 0.4083 | 0.5384 | 0.4090 | | 0.0008 | 90.0 | 4500 | 4.5574 | 0.39 | 1.1077 | 11.2517 | 0.39 | 0.3940 | 0.5618 | 0.4405 | | 0.0008 | 91.0 | 4550 | 4.5104 | 0.41 | 1.0890 | 10.8687 | 0.41 | 0.4173 | 0.5411 | 0.4350 | | 0.0008 | 92.0 | 4600 | 4.3791 | 0.425 | 1.0672 | 10.7198 | 0.425 | 0.4202 | 0.5233 | 0.4306 | | 0.0008 | 93.0 | 4650 | 4.3608 | 0.43 | 1.0553 | 10.8428 | 0.4300 | 0.4236 | 0.5196 | 0.4284 | | 0.0008 | 94.0 | 4700 | 4.3469 | 0.44 | 1.0474 | 10.6774 | 0.44 | 0.4428 | 0.5020 | 0.4280 | | 0.0008 | 95.0 | 4750 | 4.3420 | 0.44 | 1.0487 | 10.5138 | 0.44 | 0.4385 | 0.5260 | 0.4270 | | 0.0008 | 96.0 | 4800 | 4.3385 | 0.435 | 1.0491 | 10.3448 | 0.435 | 0.4312 | 0.5170 | 0.4266 | | 0.0008 | 97.0 | 4850 | 4.3341 | 0.435 | 1.0485 | 10.3378 | 0.435 | 0.4312 | 0.5136 | 0.4261 | | 0.0008 | 98.0 | 4900 | 4.3336 | 0.435 | 1.0480 | 10.3350 | 0.435 | 0.4312 | 0.5184 | 0.4253 | | 0.0008 | 99.0 | 4950 | 4.3306 | 0.435 | 1.0472 | 10.3328 | 0.435 | 0.4299 | 0.5116 | 0.4245 | | 0.0001 | 100.0 | 5000 | 4.3305 | 0.435 | 1.0472 | 10.3327 | 0.435 | 0.4299 | 0.5115 | 0.4245 | ### Framework versions - Transformers 4.28.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.12.0 - Tokenizers 0.12.1

km0228kr/xlm-roberta-base-finetuned-panx-de

km0228kr

2023-07-10T10:57:14Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-07-10T10:47:06Z

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.de split: validation args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8653353814644136 ---  # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1339 - F1: 0.8653 ## 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: 24 - eval_batch_size: 24 - 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 | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2583 | 1.0 | 525 | 0.1596 | 0.8231 | | 0.1262 | 2.0 | 1050 | 0.1395 | 0.8468 | | 0.0824 | 3.0 | 1575 | 0.1339 | 0.8653 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

Apocalypse-19/trocr-MICR

Apocalypse-19

2023-07-10T10:45:25Z

73

1

transformers

[ "transformers", "pytorch", "vision-encoder-decoder", "image-text-to-text", "Image-to-Text", "trocr", "en", "endpoints_compatible", "region:us" ]

image-text-to-text

2023-07-07T15:35:59Z

--- language: - en tags: - Image-to-Text - trocr --- ## TrOCR-MICR An OCR model for transcribing e13b MICR codes. Finetuned from Microsoft's [TrOCR-large-stage1](https://huggingface.co/microsoft/trocr-large-printed). Model was finetuned using the e13b portion of the MICR dataset given [here](https://github.com/DoubangoTelecom/tesseractMICR)

FelixChao/medical_faq_gpt_vicuna7b_chinese

FelixChao

2023-07-10T10:32:00Z

0

peft

[ "peft", "region:us" ]

null

2023-07-10T10:31:43Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0.dev0

crisU8/bert-finetuned-ner-clinical-plncmm-large-25

crisU8

2023-07-10T10:18:28Z

110

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-07-10T09:52:07Z

--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner-clinical-plncmm-large-25 results: [] ---  # bert-finetuned-ner-clinical-plncmm-large-25 This model is a fine-tuned version of [plncmm/beto-clinical-wl-es](https://huggingface.co/plncmm/beto-clinical-wl-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2487 - Precision: 0.7372 - Recall: 0.8035 - F1: 0.7689 - Accuracy: 0.9270 ## 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: 18 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 400 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 446 | 0.2607 | 0.6701 | 0.7772 | 0.7197 | 0.9113 | | 0.6128 | 2.0 | 892 | 0.2298 | 0.7266 | 0.7964 | 0.7599 | 0.9254 | | 0.1927 | 3.0 | 1338 | 0.2487 | 0.7372 | 0.8035 | 0.7689 | 0.9270 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

jordyvl/dit-tiny_rvl_cdip_100_examples_per_class_kd_MSE_lr_fix

jordyvl

2023-07-10T10:05:07Z

161

0

transformers

[ "transformers", "pytorch", "tensorboard", "beit", "image-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-07-10T09:48:49Z

--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: dit-tiny_rvl_cdip_100_examples_per_class_kd_MSE_lr_fix results: [] ---  # dit-tiny_rvl_cdip_100_examples_per_class_kd_MSE_lr_fix This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4358 - Accuracy: 0.195 - Brier Loss: 0.9035 - Nll: 12.0550 - F1 Micro: 0.195 - F1 Macro: 0.1471 - Ece: 0.1675 - Aurc: 0.6988 ## 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: 32 - eval_batch_size: 32 - 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: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:-------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 25 | 1.5167 | 0.07 | 0.9368 | 20.8948 | 0.07 | 0.0305 | 0.1106 | 0.8850 | | No log | 2.0 | 50 | 1.5246 | 0.08 | 0.9362 | 21.4368 | 0.08 | 0.0346 | 0.1200 | 0.8659 | | No log | 3.0 | 75 | 1.5053 | 0.1 | 0.9340 | 23.7241 | 0.1000 | 0.0522 | 0.1280 | 0.8087 | | No log | 4.0 | 100 | 1.5097 | 0.0975 | 0.9322 | 17.3004 | 0.0975 | 0.0487 | 0.1220 | 0.8220 | | No log | 5.0 | 125 | 1.4926 | 0.12 | 0.9296 | 16.3893 | 0.12 | 0.0600 | 0.1284 | 0.7752 | | No log | 6.0 | 150 | 1.4838 | 0.105 | 0.9273 | 19.3692 | 0.1050 | 0.0356 | 0.1254 | 0.7955 | | No log | 7.0 | 175 | 1.4729 | 0.0975 | 0.9229 | 18.6899 | 0.0975 | 0.0411 | 0.1134 | 0.7963 | | No log | 8.0 | 200 | 1.4754 | 0.125 | 0.9196 | 17.7842 | 0.125 | 0.0676 | 0.1238 | 0.7778 | | No log | 9.0 | 225 | 1.4725 | 0.1125 | 0.9193 | 16.6572 | 0.1125 | 0.0505 | 0.1254 | 0.7839 | | No log | 10.0 | 250 | 1.4702 | 0.1175 | 0.9168 | 16.3975 | 0.1175 | 0.0556 | 0.1183 | 0.7638 | | No log | 11.0 | 275 | 1.4648 | 0.1175 | 0.9169 | 18.4274 | 0.1175 | 0.0558 | 0.1219 | 0.7806 | | No log | 12.0 | 300 | 1.4660 | 0.155 | 0.9166 | 15.6492 | 0.155 | 0.0791 | 0.1411 | 0.7512 | | No log | 13.0 | 325 | 1.4684 | 0.16 | 0.9164 | 17.1698 | 0.16 | 0.1140 | 0.1519 | 0.7285 | | No log | 14.0 | 350 | 1.4662 | 0.1175 | 0.9158 | 17.6999 | 0.1175 | 0.0501 | 0.1269 | 0.7637 | | No log | 15.0 | 375 | 1.4602 | 0.1675 | 0.9143 | 13.2540 | 0.1675 | 0.1153 | 0.1515 | 0.7223 | | No log | 16.0 | 400 | 1.4556 | 0.1325 | 0.9138 | 13.3868 | 0.1325 | 0.0881 | 0.1323 | 0.7558 | | No log | 17.0 | 425 | 1.4527 | 0.175 | 0.9128 | 11.1983 | 0.175 | 0.1334 | 0.1596 | 0.7153 | | No log | 18.0 | 450 | 1.4535 | 0.1625 | 0.9111 | 17.6046 | 0.1625 | 0.1021 | 0.1435 | 0.7379 | | No log | 19.0 | 475 | 1.4453 | 0.1825 | 0.9086 | 11.8948 | 0.1825 | 0.1228 | 0.1594 | 0.7098 | | 1.4614 | 20.0 | 500 | 1.4431 | 0.1525 | 0.9078 | 14.2631 | 0.1525 | 0.1115 | 0.1410 | 0.7293 | | 1.4614 | 21.0 | 525 | 1.4392 | 0.1825 | 0.9063 | 10.7664 | 0.1825 | 0.1378 | 0.1567 | 0.7058 | | 1.4614 | 22.0 | 550 | 1.4469 | 0.1775 | 0.9055 | 13.4724 | 0.1775 | 0.1212 | 0.1483 | 0.7107 | | 1.4614 | 23.0 | 575 | 1.4356 | 0.17 | 0.9039 | 11.8141 | 0.17 | 0.1232 | 0.1515 | 0.7091 | | 1.4614 | 24.0 | 600 | 1.4370 | 0.1875 | 0.9039 | 12.9338 | 0.1875 | 0.1384 | 0.1539 | 0.7017 | | 1.4614 | 25.0 | 625 | 1.4358 | 0.195 | 0.9035 | 12.0550 | 0.195 | 0.1471 | 0.1675 | 0.6988 | ### Framework versions - Transformers 4.28.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.12.0 - Tokenizers 0.12.1

ducnapa/cute-cartoon-illustration

ducnapa

2023-07-10T09:59:49Z

29

5

diffusers

[ "diffusers", "text-to-image", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-06-23T10:00:04Z

--- library_name: diffusers pipeline_tag: text-to-image ---

justinpinkney/falcon-7b

justinpinkney

2023-07-10T09:49:02Z

13

0

transformers

[ "transformers", "pytorch", "RefinedWebModel", "text-generation", "custom_code", "en", "dataset:tiiuae/falcon-refinedweb", "arxiv:2205.14135", "arxiv:1911.02150", "arxiv:2101.00027", "arxiv:2005.14165", "arxiv:2104.09864", "arxiv:2306.01116", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]

text-generation

2023-07-07T14:25:17Z

--- datasets: - tiiuae/falcon-refinedweb language: - en inference: false license: apache-2.0 duplicated_from: tiiuae/falcon-7b --- # 🚀 Falcon-7B **Falcon-7B is a 7B parameters causal decoder-only model built by [TII](https://www.tii.ae) and trained on 1,500B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) enhanced with curated corpora. It is made available under the Apache 2.0 license.** *Paper coming soon* 😊. 🤗 To get started with Falcon (inference, finetuning, quantization, etc.), we recommend reading [this great blogpost fron HF](https://huggingface.co/blog/falcon)! ## Why use Falcon-7B? * **It outperforms comparable open-source models** (e.g., [MPT-7B](https://huggingface.co/mosaicml/mpt-7b), [StableLM](https://github.com/Stability-AI/StableLM), [RedPajama](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-7B-v0.1) etc.), thanks to being trained on 1,500B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) enhanced with curated corpora. See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). * **It features an architecture optimized for inference**, with FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)) and multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)). * **It is made available under a permissive Apache 2.0 license allowing for commercial use**, without any royalties or restrictions. ⚠️ **This is a raw, pretrained model, which should be further finetuned for most usecases.** If you are looking for a version better suited to taking generic instructions in a chat format, we recommend taking a look at [Falcon-7B-Instruct](https://huggingface.co/tiiuae/falcon-7b-instruct). 🔥 **Looking for an even more powerful model?** [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b) is Falcon-7B's big brother! ```python from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model = "tiiuae/falcon-7b" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, tokenizer=tokenizer, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", ) sequences = pipeline( "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:", max_length=200, do_sample=True, top_k=10, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ``` 💥 **Falcon LLMs require PyTorch 2.0 for use with `transformers`!** For fast inference with Falcon, check-out [Text Generation Inference](https://github.com/huggingface/text-generation-inference)! Read more in this [blogpost]((https://huggingface.co/blog/falcon). You will need **at least 16GB of memory** to swiftly run inference with Falcon-7B. # Model Card for Falcon-7B ## Model Details ### Model Description - **Developed by:** [https://www.tii.ae](https://www.tii.ae); - **Model type:** Causal decoder-only; - **Language(s) (NLP):** English and French; - **License:** Apache 2.0. ### Model Source - **Paper:** *coming soon*. ## Uses ### Direct Use Research on large language models; as a foundation for further specialization and finetuning for specific usecases (e.g., summarization, text generation, chatbot, etc.) ### Out-of-Scope Use Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful. ## Bias, Risks, and Limitations Falcon-7B is trained on English and French data only, and will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online. ### Recommendations We recommend users of Falcon-7B to consider finetuning it for the specific set of tasks of interest, and for guardrails and appropriate precautions to be taken for any production use. ## How to Get Started with the Model ```python from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model = "tiiuae/falcon-7b" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, tokenizer=tokenizer, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", ) sequences = pipeline( "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:", max_length=200, do_sample=True, top_k=10, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ``` ## Training Details ### Training Data Falcon-7B was trained on 1,500B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb), a high-quality filtered and deduplicated web dataset which we enhanced with curated corpora. Significant components from our curated copora were inspired by The Pile ([Gao et al., 2020](https://arxiv.org/abs/2101.00027)). | **Data source** | **Fraction** | **Tokens** | **Sources** | |--------------------|--------------|------------|-----------------------------------| | [RefinedWeb-English](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) | 79% | 1,185B | massive web crawl | | Books | 7% | 110B | | | Conversations | 6% | 85B | Reddit, StackOverflow, HackerNews | | Code | 3% | 45B | | | RefinedWeb-French | 3% | 45B | massive web crawl | | Technical | 2% | 30B | arXiv, PubMed, USPTO, etc. | The data was tokenized with the Falcon-[7B](https://huggingface.co/tiiuae/falcon-7b)/[40B](https://huggingface.co/tiiuae/falcon-40b) tokenizer. ### Training Procedure Falcon-7B was trained on 384 A100 40GB GPUs, using a 2D parallelism strategy (PP=2, DP=192) combined with ZeRO. #### Training Hyperparameters | **Hyperparameter** | **Value** | **Comment** | |--------------------|------------|-------------------------------------------| | Precision | `bfloat16` | | | Optimizer | AdamW | | | Learning rate | 6e-4 | 4B tokens warm-up, cosine decay to 1.2e-5 | | Weight decay | 1e-1 | | | Z-loss | 1e-4 | | | Batch size | 2304 | 30B tokens ramp-up | #### Speeds, Sizes, Times Training happened in early March 2023 and took about two weeks. ## Evaluation *Paper coming soon*. See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) for early results. ## Technical Specifications ### Model Architecture and Objective Falcon-7B is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token). The architecture is broadly adapted from the GPT-3 paper ([Brown et al., 2020](https://arxiv.org/abs/2005.14165)), with the following differences: * **Positionnal embeddings:** rotary ([Su et al., 2021](https://arxiv.org/abs/2104.09864)); * **Attention:** multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)) and FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)); * **Decoder-block:** parallel attention/MLP with a single layer norm. | **Hyperparameter** | **Value** | **Comment** | |--------------------|-----------|----------------------------------------| | Layers | 32 | | | `d_model` | 4544 | Increased to compensate for multiquery | | `head_dim` | 64 | Reduced to optimise for FlashAttention | | Vocabulary | 65024 | | | Sequence length | 2048 | | ### Compute Infrastructure #### Hardware Falcon-7B was trained on AWS SageMaker, on 384 A100 40GB GPUs in P4d instances. #### Software Falcon-7B was trained a custom distributed training codebase, Gigatron. It uses a 3D parallelism approach combined with ZeRO and high-performance Triton kernels (FlashAttention, etc.) ## Citation *Paper coming soon* 😊. In the meanwhile, you can use the following information to cite: ``` @article{falcon40b, title={{Falcon-40B}: an open large language model with state-of-the-art performance}, author={Almazrouei, Ebtesam and Alobeidli, Hamza and Alshamsi, Abdulaziz and Cappelli, Alessandro and Cojocaru, Ruxandra and Debbah, Merouane and Goffinet, Etienne and Heslow, Daniel and Launay, Julien and Malartic, Quentin and Noune, Badreddine and Pannier, Baptiste and Penedo, Guilherme}, year={2023} } ``` To learn more about the pretraining dataset, see the 📓 [RefinedWeb paper](https://arxiv.org/abs/2306.01116). ``` @article{refinedweb, title={The {R}efined{W}eb dataset for {F}alcon {LLM}: outperforming curated corpora with web data, and web data only}, author={Guilherme Penedo and Quentin Malartic and Daniel Hesslow and Ruxandra Cojocaru and Alessandro Cappelli and Hamza Alobeidli and Baptiste Pannier and Ebtesam Almazrouei and Julien Launay}, journal={arXiv preprint arXiv:2306.01116}, eprint={2306.01116}, eprinttype = {arXiv}, url={https://arxiv.org/abs/2306.01116}, year={2023} } ``` ## License Falcon-7B is made available under the Apache 2.0 license. ## Contact [email protected]

TheBloke/GodziLLa-30B-GGML

TheBloke

2023-07-10T09:38:45Z

0

4

null

[ "merge", "mix", "cot", "text-generation", "license:other", "region:us" ]

text-generation

2023-07-09T11:53:15Z

--- inference: false license: other pipeline_tag: text-generation tags: - merge - mix - cot ---  <div style="width: 100%;"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <a href="https://discord.gg/theblokeai">Chat & support: my new Discord server</a> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a> </div> </div>  # Maya Philippine's GodziLLa 30B GGML These files are GGML format model files for [Maya Philippine's GodziLLa 30B](https://huggingface.co/MayaPH/GodziLLa-30B). GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as: * [text-generation-webui](https://github.com/oobabooga/text-generation-webui) * [KoboldCpp](https://github.com/LostRuins/koboldcpp) * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui) * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) * [ctransformers](https://github.com/marella/ctransformers) ## Licensing This model is GodziLLa-30B, a language model developed by Maya Philippines. Maya Philippines' work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. For more information, visit: https://creativecommons.org/licenses/by-nc/4.0/ This model is based on Meta LLaMA weights, which are licensed under a bespoke research-only non-commercial license. ## Repositories available * [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/GodziLLa-30B-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/GodziLLa-30B-GGML) * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/MayaPH/GodziLLa-30B) ## Prompt template: Alpaca ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: PROMPT ### Response: ```  ## Compatibility ### Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0` These are guaranteed to be compatible with any UIs, tools and libraries released since late May. They may be phased out soon, as they are largely superseded by the new k-quant methods. ### New k-quant methods: `q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K` These new quantisation methods are compatible with llama.cpp as of June 6th, commit `2d43387`. They are now also compatible with recent releases of text-generation-webui, KoboldCpp, llama-cpp-python, ctransformers, rustformers and most others. For compatibility with other tools and libraries, please check their documentation. ## Explanation of the new k-quant methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw * GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type. Refer to the Provided Files table below to see what files use which methods, and how. </details>  ## Provided files | Name | Quant method | Bits | Size | Max RAM required | Use case | | ---- | ---- | ---- | ---- | ---- | ----- | | godzilla-30b.ggmlv3.q2_K.bin | q2_K | 2 | 13.71 GB| 16.21 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. | | godzilla-30b.ggmlv3.q3_K_S.bin | q3_K_S | 3 | 14.06 GB| 16.56 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors | | godzilla-30b.ggmlv3.q3_K_M.bin | q3_K_M | 3 | 15.72 GB| 18.22 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K | | godzilla-30b.ggmlv3.q3_K_L.bin | q3_K_L | 3 | 17.28 GB| 19.78 GB | New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K | | godzilla-30b.ggmlv3.q4_K_S.bin | q4_K_S | 4 | 18.36 GB| 20.86 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors | | godzilla-30b.ggmlv3.q4_K_M.bin | q4_K_M | 4 | 19.62 GB| 22.12 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K | | godzilla-30b.ggmlv3.q4_0.bin | q4_0 | 4 | 18.30 GB| 20.80 GB | Original quant method, 4-bit. | | godzilla-30b.ggmlv3.q4_1.bin | q4_1 | 4 | 20.33 GB| 22.83 GB | Original quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. | | godzilla-30b.ggmlv3.q5_K_S.bin | q5_K_S | 5 | 22.40 GB| 24.90 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors | | godzilla-30b.ggmlv3.q5_K_M.bin | q5_K_M | 5 | 23.05 GB| 25.55 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K | | godzilla-30b.ggmlv3.q5_0.bin | q5_0 | 5 | 22.37 GB| 24.87 GB | Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. | | godzilla-30b.ggmlv3.q5_1.bin | q5_1 | 5 | 24.40 GB| 26.90 GB | Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference. | | godzilla-30b.ggmlv3.q6_K.bin | q6_K | 6 | 26.69 GB| 29.19 GB | New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors | | godzilla-30b.ggmlv3.q8_0.bin | q8_0 | 8 | 34.56 GB| 37.06 GB | Original quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. | **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. ## How to run in `llama.cpp` I use the following command line; adjust for your tastes and needs: ``` ./main -t 10 -ngl 32 -m godzilla-30b.ggmlv3.q4_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: Write a story about llamas\n### Response:" ``` Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`. Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md).  ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute. Thanks to the [chirper.ai](https://chirper.ai) team! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Luke from CarbonQuill, Aemon Algiz. **Patreon special mentions**: RoA, Lone Striker, Gabriel Puliatti, Derek Yates, Randy H, Jonathan Leane, Eugene Pentland, Karl Bernard, Viktor Bowallius, senxiiz, Daniel P. Andersen, Pierre Kircher, Deep Realms, Cory Kujawski, Oscar Rangel, Fen Risland, Ajan Kanaga, LangChain4j, webtim, Nikolai Manek, Trenton Dambrowitz, Raven Klaugh, Kalila, Khalefa Al-Ahmad, Chris McCloskey, Luke @flexchar, Ai Maven, Dave, Asp the Wyvern, Sean Connelly, Imad Khwaja, Space Cruiser, Rainer Wilmers, subjectnull, Alps Aficionado, Willian Hasse, Fred von Graf, Artur Olbinski, Johann-Peter Hartmann, WelcomeToTheClub, Willem Michiel, Michael Levine, Iucharbius , Spiking Neurons AB, K, biorpg, John Villwock, Pyrater, Greatston Gnanesh, Mano Prime, Junyu Yang, Stephen Murray, John Detwiler, Luke Pendergrass, terasurfer , Pieter, zynix , Edmond Seymore, theTransient, Nathan LeClaire, vamX, Kevin Schuppel, Preetika Verma, ya boyyy, Alex , SuperWojo, Ghost , Joseph William Delisle, Matthew Berman, Talal Aujan, chris gileta, Illia Dulskyi. Thank you to all my generous patrons and donaters!  # Original model card: Maya Philippine's GodziLLa 30B <img src="https://drive.google.com/uc?export=view&id=16DzZwhqybQvT1wQVp-6qXHI9HhKft6CR" width="50%" alt="GodziLLa-30B"> Released July 9, 2023 ## Model Description GodziLLa-30B is an experimental combination of various proprietary Maya LoRAs with CalderaAI's [Lazarus-30B](https://huggingface.co/CalderaAI/30B-Lazarus). This composite model is not meant for any other use outside of research on competing LoRA adapter behavior. More specifically, since this is inherently a LlaMA model, **commercial use is prohibited**. This model's primary purpose is to stress test the limitations of composite LLMs and observe its performance with respect to other LLMs available on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). ![Godzilla Let Them Fight Meme GIF](https://media.tenor.com/AZkmVImwd5YAAAAC/godzilla-let-them-fight.gif) ## Recommended Prompt Format Alpaca's instruction is the recommended prompt format, but Vicuna's instruction format may also work. ## Usage To use GodziLLa-30B, you are required to provide attribution in accordance with the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. Please include the following attribution notice when utilizing GodziLLa-30B in your work: ```python # This code uses GodziLLa-30B, a language model developed by Maya Philippines. # The model is licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. # For more information, visit: https://creativecommons.org/licenses/by-nc/4.0/ from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("MayaPH/GodziLLa-30B") model = AutoModelForCausalLM.from_pretrained("MayaPH/GodziLLa-30B") ``` Please ensure that you include the relevant attribution notice in your code or any other form of usage and restrict your usage to non-commercial use to comply with the license terms. ## Ethical Considerations When using GodziLLa-30B, it is important to consider the following ethical considerations: 1. **Privacy and Security:** Avoid sharing sensitive personal information while interacting with the model. The model does not have privacy safeguards, so exercise caution when discussing personal or confidential matters. 2. **Fairness and Bias:** The model's responses may reflect biases present in the training data. Be aware of potential biases and make an effort to evaluate responses critically and fairly. 3. **Transparency:** The model operates as a predictive text generator based on patterns learned from the training data. The model's inner workings and the specific training data used are proprietary and not publicly available. 4. **User Responsibility:** Users should take responsibility for their own decisions and not solely rely on the information provided by the model. Consult with the appropriate professionals or reliable sources for specific advice or recommendations. 5. **NSFW Content:** The model is a merge of multiple model checkpoints and LoRA adapters. It is highly likely that the resulting model contains uncensored content that may include, but is not limited to, violence, gore, explicit language, and sexual content. If you plan to further refine this model for safe/aligned usage, you are highly encouraged to implement guardrails along with it. ## Further Information For additional information or inquiries about GodziLLa-30B, please contact the Maya Philippines iOps Team via [email protected]. ## Disclaimer GodziLLa-30B is an AI language model from Maya Philippines. It is provided "as is" without warranty of any kind, express or implied. The model developers and Maya Philippines shall not be liable for any direct or indirect damages arising from the use of this model. ## Acknowledgments The development of GodziLLa-30B was made possible by Maya Philippines and the curation of the various proprietary datasets and creation of the different proprietary LoRA adapters.

ArimaKana38/alpaca-cmkl

ArimaKana38

2023-07-10T09:33:02Z

2

0

peft

[ "peft", "region:us" ]

null

2023-07-08T11:17:05Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0

Arindam75/a2c-PandaReachDense-v2

Arindam75

2023-07-10T09:27:33Z

0

stable-baselines3

[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-10T09:24:51Z

--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -1.60 +/- 0.59 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v2** This is a trained model of a **A2C** agent playing **PandaReachDense-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 ... ```

nolanaatama/stnmrshsthprkrvcv2300pchrhys

nolanaatama

2023-07-10T09:19:39Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-07-10T09:16:40Z

--- license: creativeml-openrail-m ---

Salama1429/TTS_German_Speecht5_finetuned_voxpopuli_nl

Salama1429

2023-07-10T09:13:40Z

326

0

transformers

[ "transformers", "pytorch", "tensorboard", "speecht5", "text-to-audio", "generated_from_trainer", "text-to-speech", "nl", "dataset:facebook/voxpopuli", "license:mit", "endpoints_compatible", "region:us" ]

text-to-speech

2023-07-10T06:43:53Z

--- language: - nl license: mit tags: - generated_from_trainer datasets: - facebook/voxpopuli model-index: - name: TTS_German_Speecht5_finetuned_voxpopuli_nl results: [] pipeline_tag: text-to-speech ---  # TTS_German_Speecht5_finetuned_voxpopuli_nl This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the facebook/voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.4593 ## 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: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.5248 | 4.3 | 1000 | 0.4792 | | 0.5019 | 8.61 | 2000 | 0.4663 | | 0.4937 | 12.91 | 3000 | 0.4609 | | 0.4896 | 17.21 | 4000 | 0.4593 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

crisU8/bert-finetuned-ner-clinical-plncmm-large-22

crisU8

2023-07-10T09:04:25Z

109

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-07-10T08:59:40Z

--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner-clinical-plncmm-large-22 results: [] ---  # bert-finetuned-ner-clinical-plncmm-large-22 This model is a fine-tuned version of [plncmm/beto-clinical-wl-es](https://huggingface.co/plncmm/beto-clinical-wl-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2380 - Precision: 0.7554 - Recall: 0.8271 - F1: 0.7896 - Accuracy: 0.9320 ## 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: 24 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 400 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.5983 | 1.0 | 572 | 0.2405 | 0.7044 | 0.7964 | 0.7476 | 0.9227 | | 0.1979 | 2.0 | 1144 | 0.2421 | 0.7296 | 0.8189 | 0.7717 | 0.9275 | | 0.1406 | 3.0 | 1716 | 0.2380 | 0.7554 | 0.8271 | 0.7896 | 0.9320 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

JensTiburski/test

JensTiburski

2023-07-10T09:00:29Z

0

null

[ "de", "region:us" ]

null

2023-07-10T08:56:13Z

--- language: - de --- from diffusers import StableDiffusionLDM3DPipeline pipe_ldm3d = StableDiffusionLDM3DPipeline.from_pretrained("Intel/ldm3d") prompt = "A picture of a castle in the mountains" output = pipe_ldm3d(prompt) obj_file = output.obj obj_file[0].save("castle_ldm3d.obj") --- license: cc ---

soBeauty/xlm-roberta-base-09072023-revised_2

soBeauty

2023-07-10T08:59:49Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "fill-mask", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-07-09T15:31:18Z

--- license: mit tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlm-roberta-base-09072023-revised_2 results: [] ---  # xlm-roberta-base-09072023-revised_2 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Accuracy: 0.5577 - Loss: 2.2632 ## 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: 10 ### Training results | Training Loss | Epoch | Step | Accuracy | Validation Loss | |:-------------:|:-----:|:----:|:--------:|:---------------:| | 2.725 | 3.85 | 500 | 0.4944 | 2.4300 | | 2.5409 | 7.69 | 1000 | 0.5577 | 2.2632 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

Aeala/Chronoboros-33b-4bit

Aeala

2023-07-10T08:48:19Z

5

0

transformers

[ "transformers", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2023-07-10T06:52:52Z

4-bit GPTQ quantization of the [chronoboros-33b](https://huggingface.co/Henk717/chronoboros-33B) merge.

danbrown/checkpoints

danbrown

2023-07-10T08:43:17Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-07-10T08:20:33Z

--- license: creativeml-openrail-m --- This is a collection of Stable Diffusion model checkpoints, just like my other loras collection. I may be listing it with more details here as I add the models. The models here can be third-party checkpoints, or personal experiments.

SpringYung/falcon_with_10latex

SpringYung

2023-07-10T08:42:21Z

0

peft

[ "peft", "region:us" ]

null

2023-07-10T08:41:51Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.4.0.dev0

SpringYung/dolly_with_10examples

SpringYung

2023-07-10T08:30:27Z

0

peft

[ "peft", "region:us" ]

null

2023-07-10T08:30:04Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.4.0.dev0

crisU8/bert-finetuned-ner-clinical-plncmm-large-15

crisU8

2023-07-10T08:15:12Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-07-10T07:57:20Z

--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner-clinical-plncmm-large-15 results: [] ---  # bert-finetuned-ner-clinical-plncmm-large-15 This model is a fine-tuned version of [plncmm/beto-clinical-wl-es](https://huggingface.co/plncmm/beto-clinical-wl-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2339 - Precision: 0.7526 - Recall: 0.8282 - F1: 0.7886 - Accuracy: 0.9309 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 400 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 429 | 0.2466 | 0.6954 | 0.7958 | 0.7423 | 0.9223 | | 0.5736 | 2.0 | 858 | 0.2380 | 0.7354 | 0.8178 | 0.7744 | 0.9264 | | 0.1845 | 3.0 | 1287 | 0.2339 | 0.7526 | 0.8282 | 0.7886 | 0.9309 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

komo-dono/matsuoka

komo-dono

2023-07-10T08:14:09Z

0

null

[ "region:us" ]

null

2023-07-10T08:12:42Z

--- license: openrail language: - ja tags: - music matsuoka 500 epoch

guaguale/model_kthv_vcg

guaguale

2023-07-10T08:11:44Z

0

diffusers

[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-07-09T18:50:52Z

--- license: creativeml-openrail-m base_model: /mmu_vcg_ssd/liuhao12/workspace/1_diffusion/models/sd-zhuxiongwei-320nodes-task3_2-0612/checkpoint-28000/ instance_prompt: a male idol sks with blonde hair, wearing a black jacket and fringes on the sides of the jacket tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - guaguale/model_kthv_vcg This is a dreambooth model derived from /mmu_vcg_ssd/liuhao12/workspace/1_diffusion/models/sd-zhuxiongwei-320nodes-task3_2-0612/checkpoint-28000/. The weights were trained on a male idol sks with blonde hair, wearing a black jacket and fringes on the sides of the jacket using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.

lloydchang/wongstein-vide-noir

lloydchang

2023-07-10T07:49:17Z

207

1

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "text-generation-inference", "en", "dataset:amazon_us_reviews", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2023-07-10T07:43:16Z

--- license: creativeml-openrail-m datasets: - amazon_us_reviews language: - en tags: - text-generation-inference ---

crisU8/bert-finetuned-ner-clinical-plncmm-large-13

crisU8

2023-07-10T07:39:09Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-07-10T07:21:14Z

--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner-clinical-plncmm-large-13 results: [] ---  # bert-finetuned-ner-clinical-plncmm-large-13 This model is a fine-tuned version of [plncmm/beto-clinical-wl-es](https://huggingface.co/plncmm/beto-clinical-wl-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2336 - Precision: 0.7488 - Recall: 0.8227 - F1: 0.7840 - Accuracy: 0.9308 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 300 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 429 | 0.2408 | 0.7044 | 0.8123 | 0.7545 | 0.9223 | | 0.5338 | 2.0 | 858 | 0.2382 | 0.7322 | 0.8178 | 0.7726 | 0.9264 | | 0.1771 | 3.0 | 1287 | 0.2336 | 0.7488 | 0.8227 | 0.7840 | 0.9308 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

NasimB/gpt2-concat-guten-mod-rm-ref-2k-rarity-2p5k-p13k

NasimB

2023-07-10T07:28:38Z

5

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-10T05:33:27Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: gpt2-concat-guten-mod-rm-ref-2k-rarity-2p5k-p13k results: [] ---  # gpt2-concat-guten-mod-rm-ref-2k-rarity-2p5k-p13k This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 3.1752 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.7031 | 0.29 | 500 | 5.6491 | | 5.3381 | 0.59 | 1000 | 5.2075 | | 4.9932 | 0.88 | 1500 | 4.9530 | | 4.7151 | 1.17 | 2000 | 4.8058 | | 4.5556 | 1.46 | 2500 | 4.6811 | | 4.4516 | 1.76 | 3000 | 4.5752 | | 4.3291 | 2.05 | 3500 | 4.4930 | | 4.1341 | 2.34 | 4000 | 4.4457 | | 4.1006 | 2.63 | 4500 | 4.3896 | | 4.0621 | 2.93 | 5000 | 4.3371 | | 3.8509 | 3.22 | 5500 | 4.3335 | | 3.8058 | 3.51 | 6000 | 4.2974 | | 3.7835 | 3.81 | 6500 | 4.2701 | | 3.6851 | 4.1 | 7000 | 4.2656 | | 3.5155 | 4.39 | 7500 | 4.2594 | | 3.5136 | 4.68 | 8000 | 4.2428 | | 3.5037 | 4.98 | 8500 | 4.2302 | | 3.3411 | 5.27 | 9000 | 4.2422 | | 3.321 | 5.56 | 9500 | 4.2417 | | 3.323 | 5.85 | 10000 | 4.2412 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

chizhikchi/sci-five-radsum23

chizhikchi

2023-07-10T07:27:40Z

209

0

transformers

[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "summarization", "medical", "clinical", "en", "dataset:MIMIC-III", "license:afl-3.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

summarization

2023-05-02T10:22:12Z

--- license: afl-3.0 tags: - summarization - t5 - medical - clinical language: en datasets: - MIMIC-III widget: - again noted is the large intraparenchymal hemorrhage in the posterior right frontal lobe with extension into both lateral ventricles. the degree of surrounding edema and effacement of adjacent sulci is unchanged. there is minor contralateral shift of normal midline structures. the ventricular size is unchanged. subarachnoid blood is now seen in the left frontal and parietal lobes, likely due to recirculation of the ventricular blood. - a least two attempts were made at imaging, however, the study remains severely limited by patient motion. minimal hyperdensity tracks along a left parietal sulcus (2a:18) is equivocal for a small subarachnoid hemorhage. there is no large mass effect detected. there is no shift of normally midline structures. a minimally displaced zygomatic fracture is present (2a:9). the middle ear cavities, mastoid air cells are clear. there is extensive soft tissue swelling overlying the right frontal calvarium with swelling extending to the right preseptal soft tissues (2a:12). there is mild - moderate mucosal thickening within the ethmoid and maxillary sinuses with some fluid and fluid mucosal thickening in the sphenoid sinus. inference: parameters: max_length: 350 metrics: - rouge-l --- # Impression section Generator For Radiology Reports 🏥 This model is is the result of participation of SINAI team in [Task 1B: Radiology Report Summarization](https://vilmedic.app/misc/bionlp23/sharedtask) at the BioNLP workshop held on ACL 2023. The goal of this task is to foster development of automatic radiology report summarization systems and expanding their applicability by incorporating seven different modalities and anatomies in the provided data. We propose to automate the generation of radiology impressions with "sequence-to-sequence" learning that leverages the power of publicly available pre-trained models, both general domain and biomedical domain-specific. This repository provides access to our best-performing system that resulted from fine-tuning of [Sci-Five base](https://huggingface.co/razent/SciFive-base-Pubmed_PMC), which is T5 model trained for extra 200k steps to optimize it in the context of biomedical literature. # Results The official evaluation results prove that adaptation of a general-domain system for biomedical literature is beneficial for the subsequent fine-tuning for radiology report summarization task. The Table below summarizes the official scores obtained by this model during the official evaluation. Team standings re available [here](https://vilmedic.app/misc/bionlp23/leaderboard/). | BLEU4 | ROUGE-L | BERTscore | F1-RadGraph |-----------|--------|----------|----------| | 017.38 | 32.32 | 55.04 | 33.96 | # System description paper and citation The paper with the detailed description of the system is published in the [Proceedings of the 22st Workshop on Biomedical Language Processing](https://aclanthology.org/2023.bionlp-1.53/). BibTeX citation: ``` @inproceedings{chizhikova-etal-2023-sinai, title = "{SINAI} at {R}ad{S}um23: Radiology Report Summarization Based on Domain-Specific Sequence-To-Sequence Transformer Model", author = "Chizhikova, Mariia and Diaz-Galiano, Manuel and Urena-Lopez, L. Alfonso and Martin-Valdivia, M. Teresa", booktitle = "The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.bionlp-1.53", pages = "530--534", abstract = "This paper covers participation of the SINAI team in the shared task 1B: Radiology Report Summarization at the BioNLP workshop held on ACL 2023. Our proposal follows a sequence-to-sequence approach which leverages pre-trained multilingual general domain and monolingual biomedical domain pre-trained language models. The best performing system based on domain-specific model reached 33.96 F1RadGraph score which is the fourth best result among the challenge participants. This model was made publicly available on HuggingFace. We also describe an attempt of Proximal Policy Optimization Reinforcement Learning that was made in order to improve the factual correctness measured with F1RadGraph but did not lead to satisfactory results.", } ```

zhundred/Taxi-v3

zhundred

2023-07-10T07:27:01Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-07-10T07:26:59Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 2.73 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="zhundred/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

zwtharry/ppo-Huggy

zwtharry

2023-07-10T07:13:03Z

14

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]

reinforcement-learning

2023-07-10T07:12:52Z

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: zwtharry/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

yuuhan/roberta-base-rte-lora-layer6-11

yuuhan

2023-07-10T07:04:43Z

3

0

peft

[ "peft", "region:us" ]

null

2023-07-07T02:32:01Z

--- library_name: peft --- ## Training procedure ### Framework versions RTE acc: 0.7364620938628159 - PEFT 0.4.0.dev0

crisU8/bert-finetuned-ner-clinical-plncmm-large-11

crisU8

2023-07-10T07:00:16Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-07-10T06:38:46Z

--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner-clinical-plncmm-large-11 results: [] ---  # bert-finetuned-ner-clinical-plncmm-large-11 This model is a fine-tuned version of [plncmm/beto-clinical-wl-es](https://huggingface.co/plncmm/beto-clinical-wl-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2334 - Precision: 0.7534 - Recall: 0.8216 - F1: 0.7860 - Accuracy: 0.9328 ## 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: 32 - eval_batch_size: 32 - 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 429 | 0.2492 | 0.6813 | 0.7849 | 0.7294 | 0.9211 | | 0.6251 | 2.0 | 858 | 0.2420 | 0.7467 | 0.8189 | 0.7812 | 0.9288 | | 0.1942 | 3.0 | 1287 | 0.2334 | 0.7534 | 0.8216 | 0.7860 | 0.9328 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

hoanghoavienvo/bert-large-uncased-detect-depression-stage-one

hoanghoavienvo

2023-07-10T06:55:05Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-07-09T22:16:38Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-large-uncased-detect-depression-stage-one results: [] ---  # bert-large-uncased-detect-depression-stage-one This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 0.7875 - eval_accuracy: 0.752 - eval_f1: 0.8092 - eval_runtime: 112.7187 - eval_samples_per_second: 8.872 - eval_steps_per_second: 2.218 - epoch: 3.0 - step: 4506 ## 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-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

hell1/rare-puppers

hell1

2023-07-10T06:39:55Z

194

0

transformers

[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-07-10T06:39:48Z

--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: rare-puppers results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.939393937587738 --- # rare-puppers Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### corgi ![corgi](images/corgi.jpg) #### samoyed ![samoyed](images/samoyed.jpg) #### shiba inu ![shiba inu](images/shiba_inu.jpg)

charqican/nominal-groups-recognition-bert-base-spanish-wwm-cased

charqican

2023-07-10T06:37:54Z

118

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "es", "dataset:charqican/spanish_nominal_groups_conll2003", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-07-10T06:30:03Z

--- language: - es tags: - generated_from_trainer datasets: - charqican/spanish_nominal_groups_conll2003 model-index: - name: nominal-groups-recognition-bert-base-spanish-wwm-cased results: [] ---  # nominal-groups-recognition-bert-base-spanish-wwm-cased This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on the charqican/spanish_nominal_groups_conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.2772 - Ng Precision: 0.7140 - Ng Recall: 0.7695 - Ng F1: 0.7407 - Ng Number: 3198 - Overall Precision: 0.7140 - Overall Recall: 0.7695 - Overall F1: 0.7407 - Overall Accuracy: 0.8993 ## 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: 13 - 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 | Ng Precision | Ng Recall | Ng F1 | Ng Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------------:|:---------:|:------:|:---------:|:-----------------:|:--------------:|:----------:|:----------------:| | 0.3988 | 1.0 | 228 | 0.2792 | 0.7108 | 0.7577 | 0.7335 | 3198 | 0.7108 | 0.7577 | 0.7335 | 0.8935 | | 0.2257 | 2.0 | 456 | 0.2772 | 0.7140 | 0.7695 | 0.7407 | 3198 | 0.7140 | 0.7695 | 0.7407 | 0.8993 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

crisU8/bert-finetuned-ner-clinical-plncmm-large-9

crisU8

2023-07-10T06:27:42Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-07-10T06:18:40Z

--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner-clinical-plncmm-large-9 results: [] ---  # bert-finetuned-ner-clinical-plncmm-large-9 This model is a fine-tuned version of [plncmm/beto-clinical-wl-es](https://huggingface.co/plncmm/beto-clinical-wl-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2499 - Precision: 0.7544 - Recall: 0.8244 - F1: 0.7878 - Accuracy: 0.9331 ## 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: 32 - eval_batch_size: 32 - 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: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 429 | 0.2543 | 0.6951 | 0.7794 | 0.7348 | 0.9190 | | 0.641 | 2.0 | 858 | 0.2443 | 0.7410 | 0.8117 | 0.7748 | 0.9273 | | 0.1943 | 3.0 | 1287 | 0.2388 | 0.7378 | 0.8156 | 0.7748 | 0.9304 | | 0.1211 | 4.0 | 1716 | 0.2499 | 0.7544 | 0.8244 | 0.7878 | 0.9331 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

Sukmin/ppo-SnowballTarget

Sukmin

2023-07-10T06:25:07Z

4

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]

reinforcement-learning

2023-07-10T06:22:30Z

--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Sukmin/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

jhan405/sd-class-butterflies-64

jhan405

2023-07-10T06:22:38Z

30

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2023-07-10T06:21:51Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('jhan405/sd-class-butterflies-64') image = pipeline().images[0] image ```

crisU8/bert-finetuned-ner-clinical-plncmm-large-7

crisU8

2023-07-10T06:09:28Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-07-10T06:00:18Z

--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner-clinical-plncmm-large-7 results: [] ---  # bert-finetuned-ner-clinical-plncmm-large-7 This model is a fine-tuned version of [plncmm/beto-clinical-wl-es](https://huggingface.co/plncmm/beto-clinical-wl-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2317 - Precision: 0.7503 - Recall: 0.8227 - F1: 0.7848 - Accuracy: 0.9326 ## 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: 32 - eval_batch_size: 32 - 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 429 | 0.2497 | 0.6860 | 0.7794 | 0.7297 | 0.9201 | | 0.6187 | 2.0 | 858 | 0.2391 | 0.7384 | 0.8134 | 0.7741 | 0.9293 | | 0.1936 | 3.0 | 1287 | 0.2317 | 0.7503 | 0.8227 | 0.7848 | 0.9326 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

digiplay/TEXT

digiplay

2023-07-10T06:07:16Z

0

null

[ "license:other", "region:us" ]

null

2023-07-10T06:00:56Z

--- license: other --- **TEXTUAL INVERSION** Speech Bubble https://civitai.com/models/103237/speech-bubble Trigger Words: ***SpeechBubble-Test*** ![Screenshot_20230710_140542_Vivaldi Browser Snapshot.jpg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/pdJIqCOyXh1crJ2jIW2jk.jpeg)

edgamer/Taxi-v3

edgamer

2023-07-10T05:50:16Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-07-10T05:34:15Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="edgamer/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

Sukmin/Reinforce-PixelCopter

Sukmin

2023-07-10T05:46:30Z

0

null

[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-07-10T03:34:22Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-PixelCopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 35.40 +/- 26.24 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

jhan405/sd-class-butterflies-32

jhan405

2023-07-10T05:41:33Z

30

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2023-07-10T05:40:27Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('jhan405/sd-class-butterflies-32') image = pipeline().images[0] image ```

desplesda/marian-finetuned-kde4-en-to-fr

desplesda

2023-07-10T05:39:43Z

102

0

transformers

[ "transformers", "pytorch", "marian", "text2text-generation", "translation", "generated_from_trainer", "dataset:kde4", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-07-09T14:25:59Z

--- license: apache-2.0 tags: - translation - generated_from_trainer datasets: - kde4 metrics: - bleu model-index: - name: marian-finetuned-kde4-en-to-fr results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: kde4 type: kde4 config: en-fr split: train args: en-fr metrics: - name: Bleu type: bleu value: 52.86847876856917 ---  # marian-finetuned-kde4-en-to-fr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset. It achieves the following results on the evaluation set: - Loss: 0.8559 - Bleu: 52.8685 ## 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: 64 - 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 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

edgamer/q-FrozenLake-v1-4x4-noSlippery

edgamer

2023-07-10T05:29:25Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-07-10T05:29:22Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="edgamer/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

georgeleung30/bert-finetuned-n2c2-ner

georgeleung30

2023-07-10T05:26:17Z

103

1

transformers

[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-07-10T05:19:27Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-n2c2-ner results: [] ---  # bert-finetuned-n2c2-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2116 - Precision: 0.9059 - Recall: 0.8858 - F1: 0.8958 - Accuracy: 0.9758 ## 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: 4 - 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:------:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0545 | 1.0 | 10469 | 0.1255 | 0.8929 | 0.8933 | 0.8931 | 0.9762 | | 0.0639 | 2.0 | 20938 | 0.1136 | 0.8933 | 0.8784 | 0.8858 | 0.9747 | | 0.0452 | 3.0 | 31407 | 0.1221 | 0.8864 | 0.8991 | 0.8927 | 0.9753 | | 0.0284 | 4.0 | 41876 | 0.1453 | 0.9003 | 0.8821 | 0.8911 | 0.9756 | | 0.0269 | 5.0 | 52345 | 0.1587 | 0.9011 | 0.8934 | 0.8972 | 0.9765 | | 0.0202 | 6.0 | 62814 | 0.1756 | 0.9190 | 0.8660 | 0.8917 | 0.9755 | | 0.0153 | 7.0 | 73283 | 0.1818 | 0.9063 | 0.8831 | 0.8945 | 0.9757 | | 0.0119 | 8.0 | 83752 | 0.2012 | 0.9163 | 0.8744 | 0.8948 | 0.9760 | | 0.0122 | 9.0 | 94221 | 0.1986 | 0.9001 | 0.8908 | 0.8954 | 0.9757 | | 0.0073 | 10.0 | 104690 | 0.2116 | 0.9059 | 0.8858 | 0.8958 | 0.9758 | ### Framework versions - Transformers 4.30.2 - Pytorch 1.8.1+cu111 - Datasets 2.13.1 - Tokenizers 0.13.3

Martinez66/Helluva_Boss_FastyDubs_AllStars

Martinez66

2023-07-10T05:14:24Z

0

null

[ "license:openrail", "region:us" ]

null

2023-07-10T04:06:48Z

--- license: openrail --- This is a Spanish version Here you will find the majority of the voices that have participated in Helluva Boss in Spanish, in the Fasty Dubs channel below its configuration and algorithms are shown: Epochs Type Name Voice 180 harvest Agente-1-FalloSinCovers @FalloSinCovers 175 harvest Agente-2-PatriciaAzan @PatriciaAzan 240 harvest Andrealphus-RodoBalderasLocutor @RodoBalderasLocutor 800 harvest Blitzo-Fasty-Dubs @FastyDubs 205 harvest Chazwick-Thurman-RyusakiDub @RyusakiDub 160 harvest Cletus-GeovarockFandubs @GeovarockFandubs 200 harvest Collin-Blowzyblue @blowzyblue 210 mangio-crepe 60 Crimson-Maximtru5314 @maximtru5314 250 mangio-crepe 64 Deerie-KarlyPerCastle @KarlyPerCastle 200 mangio-crepe 164 Fizzarolli-Pafoparasito @pafoparasito 300 magio-crepe 64 Keenie-UnBacconFrito @UnBacconFrito 550 harvest Loona-Hakusagiart @hakusagiart 240 magio-crepe 64 Loopty-Goopty-Pafoparasito @pafoparasito 350 crepe Lyle-Lipton-Guonejo @Guonejo 750 harvest Millie-Karu @karucovers_gt 780 harvest Moxxie-Rubecai @Rubecai 650 harvest Ozzie-Asmodeo-Cancion-Maximtru5314 @SantiagoVoiceo Dialogos-SantiagoVoiceo @maximtru5314 420 harvest Octavia-Mary-Ciel @Jenncathvoice 200 magio-crepe 40 Policia-Guonejo @Guonejo 450 harvest Stella-Sacrefleur https://www.instagram.com/sacrefleur/ 750 harvest Stolas-LaWeaAstral @LaWeaAstral 400 harvest Striker-Azzydubs5621 @azzydubs5621 600 magio-crepe 64 Verosika-Mayday-KarlyPerCastle @KarlyPerCastle 300 harvest Vortex-RodoBalderas @RodoBalderasLocutor 200 harvest Wally-Wackford-RyusakiDub @RyusakiDub Important: If you are going to use these voices make sure you give credit to the people who brought it to life. Give credits to the credro of the voices ::::::::::::::::::::::::::::::::::::::::::: Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for fair use for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.

crisU8/bert-finetuned-ner-clinical-plncmm-large-4

crisU8

2023-07-10T05:12:57Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-07-10T05:01:35Z

--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner-clinical-plncmm-large-4 results: [] ---  # bert-finetuned-ner-clinical-plncmm-large-4 This model is a fine-tuned version of [plncmm/beto-clinical-wl-es](https://huggingface.co/plncmm/beto-clinical-wl-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2677 - Precision: 0.7664 - Recall: 0.8337 - F1: 0.7986 - Accuracy: 0.9369 ## 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: 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_steps: 500 - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.6626 | 1.0 | 857 | 0.2329 | 0.7139 | 0.8068 | 0.7575 | 0.9251 | | 0.1834 | 2.0 | 1714 | 0.2479 | 0.7246 | 0.8216 | 0.7701 | 0.9268 | | 0.1162 | 3.0 | 2571 | 0.2504 | 0.7616 | 0.8310 | 0.7948 | 0.9336 | | 0.0862 | 4.0 | 3428 | 0.2677 | 0.7664 | 0.8337 | 0.7986 | 0.9369 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

smangrul/peft-lora-codegen-25-guanaco-v100-colab

smangrul

2023-07-10T05:11:58Z

9

4

peft

[ "peft", "tensorboard", "generated_from_trainer", "base_model:Salesforce/codegen25-7b-multi_P", "base_model:adapter:Salesforce/codegen25-7b-multi_P", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2023-07-08T10:24:14Z

--- license: apache-2.0 base_model: Salesforce/codegen25-7b-multi tags: - generated_from_trainer model-index: - name: peft-lora-codgen-25-guanaco-t4-colab results: [] library_name: peft ---  # peft-lora-codgen-25-guanaco-t4-colab This model is a fine-tuned version of [Salesforce/codegen25-7b-multi](https://huggingface.co/Salesforce/codegen25-7b-multi) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - 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 0.4.0.dev0 - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

Minggu/anizhchie2

Minggu

2023-07-10T04:51:49Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-07-10T04:48:59Z

--- license: creativeml-openrail-m ---

hegelty/KcBERT-Large-finetuned-josa-2

hegelty

2023-07-10T04:45:34Z

80

0

transformers

[ "transformers", "tf", "bert", "fill-mask", "generated_from_keras_callback", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-07-10T02:30:56Z

--- tags: - generated_from_keras_callback model-index: - name: hegelty/KcBERT-Large-finetuned-josa-2 results: [] ---  # hegelty/KcBERT-Large-finetuned-josa-2 This model is a fine-tuned version of [beomi/KcBERT-Large](https://huggingface.co/beomi/KcBERT-Large) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0039 - Validation Loss: 0.0000 - Epoch: 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 81390, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.0039 | 0.0000 | 0 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.9.2 - Datasets 2.13.1 - Tokenizers 0.13.3

crisU8/bert-finetuned-ner-clinical-plncmm-large-1

crisU8

2023-07-10T04:42:21Z

124

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-07-10T04:30:54Z

--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner-clinical-plncmm-large-1 results: [] ---  # bert-finetuned-ner-clinical-plncmm-large-1 This model is a fine-tuned version of [plncmm/beto-clinical-wl-es](https://huggingface.co/plncmm/beto-clinical-wl-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2406 - Precision: 0.7503 - Recall: 0.8227 - F1: 0.7848 - Accuracy: 0.9318 ## 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.3906 | 1.0 | 857 | 0.2271 | 0.7130 | 0.8101 | 0.7585 | 0.9253 | | 0.1758 | 2.0 | 1714 | 0.2378 | 0.7460 | 0.8222 | 0.7822 | 0.9290 | | 0.125 | 3.0 | 2571 | 0.2406 | 0.7503 | 0.8227 | 0.7848 | 0.9318 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

Raj-Sanjay-Shah/babyLM_roberta_base_epoch_5

Raj-Sanjay-Shah

2023-07-10T04:40:28Z

105

0

transformers

[ "transformers", "pytorch", "roberta", "fill-mask", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-07-10T04:15:35Z

--- license: cc-by-nc-sa-4.0 ---

gameofdimension/lora-cs324-length-control

gameofdimension

2023-07-10T04:38:56Z

0

null

[ "tensorboard", "generated_from_trainer", "license:mit", "region:us" ]

null

2023-06-28T15:27:59Z

--- license: mit tags: - generated_from_trainer model-index: - name: lora-cs324-length-control results: [] ---  # lora-cs324-length-control This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2118 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.5584 | 0.16 | 500 | 3.2567 | | 3.392 | 0.32 | 1000 | 3.2473 | | 3.3584 | 0.48 | 1500 | 3.2290 | | 3.3115 | 0.64 | 2000 | 3.2196 | | 3.3041 | 0.8 | 2500 | 3.2126 | | 3.3024 | 0.96 | 3000 | 3.2118 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

bastianchinchon/nominal-groups-recognition-bert-base-spanish-wwm-cased

bastianchinchon

2023-07-10T04:34:17Z

107

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "es", "dataset:bastianchinchon/spanish_nominal_groups_conll2003", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-07-10T04:06:46Z

--- language: - es tags: - generated_from_trainer datasets: - bastianchinchon/spanish_nominal_groups_conll2003 model-index: - name: nominal-groups-recognition-bert-base-spanish-wwm-cased results: [] ---  # nominal-groups-recognition-bert-base-spanish-wwm-cased This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on the bastianchinchon/spanish_nominal_groups_conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.2836 - Body Part Precision: 0.675 - Body Part Recall: 0.7191 - Body Part F1: 0.6964 - Body Part Number: 413 - Disease Precision: 0.7177 - Disease Recall: 0.7405 - Disease F1: 0.7289 - Disease Number: 975 - Family Member Precision: 0.8276 - Family Member Recall: 0.8 - Family Member F1: 0.8136 - Family Member Number: 30 - Medication Precision: 0.8228 - Medication Recall: 0.6989 - Medication F1: 0.7558 - Medication Number: 93 - Procedure Precision: 0.5586 - Procedure Recall: 0.5820 - Procedure F1: 0.5701 - Procedure Number: 311 - Overall Precision: 0.6864 - Overall Recall: 0.7075 - Overall F1: 0.6968 - Overall Accuracy: 0.9146 ## 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: 13 - 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 | Body Part Precision | Body Part Recall | Body Part F1 | Body Part Number | Disease Precision | Disease Recall | Disease F1 | Disease Number | Family Member Precision | Family Member Recall | Family Member F1 | Family Member Number | Medication Precision | Medication Recall | Medication F1 | Medication Number | Procedure Precision | Procedure Recall | Procedure F1 | Procedure Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:----:|:---------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-----------------:|:--------------:|:----------:|:--------------:|:-----------------------:|:--------------------:|:----------------:|:--------------------:|:--------------------:|:-----------------:|:-------------:|:-----------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-----------------:|:--------------:|:----------:|:----------------:| | 0.4304 | 1.0 | 1004 | 0.2985 | 0.5620 | 0.7022 | 0.6243 | 413 | 0.7059 | 0.6944 | 0.7001 | 975 | 0.8276 | 0.8 | 0.8136 | 30 | 0.6848 | 0.6774 | 0.6811 | 93 | 0.5390 | 0.5113 | 0.5248 | 311 | 0.6415 | 0.6658 | 0.6534 | 0.9028 | | 0.2346 | 2.0 | 2008 | 0.2836 | 0.675 | 0.7191 | 0.6964 | 413 | 0.7177 | 0.7405 | 0.7289 | 975 | 0.8276 | 0.8 | 0.8136 | 30 | 0.8228 | 0.6989 | 0.7558 | 93 | 0.5586 | 0.5820 | 0.5701 | 311 | 0.6864 | 0.7075 | 0.6968 | 0.9146 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

aammari/setfit-zero-shot-classification-pbsp-p3-sev

aammari

2023-07-10T04:31:17Z

4

0

sentence-transformers

[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]

text-classification

2023-07-10T04:28:56Z

--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # setfit-zero-shot-classification-pbsp-p3-sev This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("setfit-zero-shot-classification-pbsp-p3-sev") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

ghwangbo/Korean_Finetuned_Falcon

ghwangbo

2023-07-10T04:27:34Z

2

1

peft

[ "peft", "region:us" ]

null

2023-07-06T08:32:52Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0.dev0

cwiz/llama-7b-saiga-merged

cwiz

2023-07-10T04:26:24Z

5

0

transformers

[ "transformers", "pytorch", "llama", "text-generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-09T18:56:19Z

--- license: apache-2.0 --- [Saiga](https://huggingface.co/IlyaGusev/saiga_7b_lora) merged with [LLaMa-7b](https://huggingface.co/decapoda-research/llama-7b-hf) for further finetuning.

Raj-Sanjay-Shah/babyLM_roberta_base_epoch_15

Raj-Sanjay-Shah

2023-07-10T03:36:56Z

124

0

transformers

[ "transformers", "pytorch", "roberta", "fill-mask", "license:cc-by-nc-nd-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-07-10T03:07:35Z

--- license: cc-by-nc-nd-4.0 ---

eugene-yang/colbertx-xlmr-large-tt-eng.zho

eugene-yang

2023-07-10T03:29:40Z

32

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "en", "zh", "arxiv:2201.08471", "license:mit", "endpoints_compatible", "region:us" ]

null

2023-07-10T03:01:12Z

--- license: mit language: - en - zh task_categories: - text-retrieval - zero-shot-retrieval - information-retrieval - zero-shot-information-retrieval task_ids: - passage-retrieval - cross-language-retrieval --- Model trained by [Suraj Nair](https://srnair.netlify.app/). If you use the model, please cite our paper. ```bibtex @inproceedings{colbert-x, author = {Suraj Nair and Eugene Yang and Dawn Lawrie and Kevin Duh and Paul McNamee and Kenton Murray and James Mayfield and Douglas W. Oard}, title = {Transfer Learning Approaches for Building Cross-Language Dense Retrieval Models}, booktitle = {Proceedings of the 44th European Conference on Information Retrieval (ECIR)}, year = {2022}, url = {https://arxiv.org/abs/2201.08471} } ```

casque/3DMM_V12

casque

2023-07-10T03:29:02Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-07-10T03:28:24Z

--- license: creativeml-openrail-m ---

casque/Colored_Icons_by_vizsumit

casque

2023-07-10T03:24:54Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-07-10T03:23:35Z

--- license: creativeml-openrail-m ---

casque/logo_v1-000012

casque

2023-07-10T03:21:55Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-07-10T03:20:36Z

--- license: creativeml-openrail-m ---

japoople/test

japoople

2023-07-10T03:13:18Z

0

nemo

[ "nemo", "text-classification", "en", "dataset:fka/awesome-chatgpt-prompts", "license:openrail", "region:us" ]

text-classification

2023-07-10T03:07:08Z

--- license: openrail datasets: - fka/awesome-chatgpt-prompts language: - en pipeline_tag: text-classification library_name: nemo ---

VFiona/opus-mt-en-it-finetuned_4600-en-to-it

VFiona

2023-07-10T03:06:40Z

105

0

transformers

[ "transformers", "pytorch", "marian", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-07-09T19:45:48Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: opus-mt-en-it-finetuned_4600-en-to-it results: [] ---  # opus-mt-en-it-finetuned_4600-en-to-it This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-it](https://huggingface.co/Helsinki-NLP/opus-mt-en-it) 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: 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: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | No log | 1.0 | 259 | 0.5103 | 66.2359 | 28.3391 | ### Framework versions - Transformers 4.30.2 - Pytorch 1.12.1 - Datasets 2.13.1 - Tokenizers 0.11.0

edgamer/ppo-LunarLander-v2

edgamer

2023-07-10T02:51:29Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-10T01:11:26Z

--- 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: 281.47 +/- 22.46 name: mean_reward verified: false --- # **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 ... ```

NasimB/gpt2-concat-mod-datasets-rarity1-rerun

NasimB

2023-07-10T02:49:37Z

5

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-10T00:33:42Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: gpt2-concat-mod-datasets-rarity1-rerun results: [] ---  # gpt2-concat-mod-datasets-rarity1-rerun This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 3.0263 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 7 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.7311 | 0.3 | 500 | 5.6497 | | 5.3805 | 0.59 | 1000 | 5.2065 | | 5.0306 | 0.89 | 1500 | 4.9574 | | 4.7526 | 1.18 | 2000 | 4.8142 | | 4.6058 | 1.48 | 2500 | 4.6885 | | 4.4982 | 1.78 | 3000 | 4.5904 | | 4.3593 | 2.07 | 3500 | 4.5261 | | 4.185 | 2.37 | 4000 | 4.4783 | | 4.154 | 2.66 | 4500 | 4.4233 | | 4.1262 | 2.96 | 5000 | 4.3708 | | 3.8986 | 3.26 | 5500 | 4.3804 | | 3.8767 | 3.55 | 6000 | 4.3494 | | 3.8605 | 3.85 | 6500 | 4.3124 | | 3.7194 | 4.14 | 7000 | 4.3395 | | 3.5981 | 4.44 | 7500 | 4.3194 | | 3.5952 | 4.74 | 8000 | 4.3059 | | 3.5511 | 5.03 | 8500 | 4.3089 | | 3.3393 | 5.33 | 9000 | 4.3236 | | 3.3388 | 5.62 | 9500 | 4.3220 | | 3.3443 | 5.92 | 10000 | 4.3139 | | 3.2213 | 6.22 | 10500 | 4.3304 | | 3.1851 | 6.51 | 11000 | 4.3313 | | 3.1911 | 6.81 | 11500 | 4.3317 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

nomsgadded/textual_inversion_van_gogh

nomsgadded

2023-07-10T02:47:57Z

48

0

diffusers

[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "base_model:CompVis/stable-diffusion-v1-4", "base_model:adapter:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-07-10T01:24:49Z

--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - nomsgadded/textual_inversion_van_gogh These are textual inversion adaption weights for CompVis/stable-diffusion-v1-4. You can find some example images in the following.

crisU8/bert-finetuned-ner-clinical-plncmm-8

crisU8

2023-07-10T02:41:04Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-07-10T02:31:37Z

--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner-clinical-plncmm-8 results: [] ---  # bert-finetuned-ner-clinical-plncmm-8 This model is a fine-tuned version of [plncmm/beto-clinical-wl-es](https://huggingface.co/plncmm/beto-clinical-wl-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2568 - Precision: 0.7476 - Recall: 0.8063 - F1: 0.7758 - Accuracy: 0.9277 ## 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: 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_steps: 500 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.6628 | 1.0 | 502 | 0.2590 | 0.6791 | 0.7711 | 0.7222 | 0.9103 | | 0.2168 | 2.0 | 1004 | 0.2309 | 0.7243 | 0.7975 | 0.7591 | 0.9238 | | 0.1301 | 3.0 | 1506 | 0.2568 | 0.7476 | 0.8063 | 0.7758 | 0.9277 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

harrycools/q-FrozenLake-v1-4x4-noSlippery

harrycools

2023-07-10T02:40:33Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-07-10T02:40:31Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="harrycools/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

eatrero/distilbert-base-uncased-finetuned-emotion

eatrero

2023-07-10T02:22:31Z

108

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-07-09T19:15:02Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9235 - name: F1 type: f1 value: 0.9234507249341903 ---  # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2222 - Accuracy: 0.9235 - F1: 0.9235 ## 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: 64 - eval_batch_size: 64 - 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 | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 250 | 0.3302 | 0.9005 | 0.8959 | | No log | 2.0 | 500 | 0.2222 | 0.9235 | 0.9235 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

douy/T5-11B-Ctrl-Simplification

douy

2023-07-10T02:22:04Z

24

1

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "en", "arxiv:2212.09739", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-07-10T01:44:27Z

--- license: apache-2.0 language: - en --- # Model Card for Model ID  This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description  - **Developed by:** [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]  - **Repository:** [More Information Needed] - **Paper:** [https://arxiv.org/abs/2212.09739](https://arxiv.org/abs/2212.09739) - **Demo:** [http://lens-score.com](http://lens-score.com) ## Uses  ### Direct Use  [More Information Needed] ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Chickenfish/Monica_stable_v1

Chickenfish

2023-07-10T02:19:37Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-07-10T02:17:36Z

--- license: creativeml-openrail-m ---

JBJoyce/ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan

JBJoyce

2023-07-10T01:51:39Z

161

0

transformers

[ "transformers", "pytorch", "tensorboard", "audio-spectrogram-transformer", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "license:bsd-3-clause", "endpoints_compatible", "region:us" ]

audio-classification

2023-07-10T00:20:46Z

--- license: bsd-3-clause tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan results: [] ---  # ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.4626 - Accuracy: 0.9 ## 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: 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6832 | 1.0 | 113 | 0.6136 | 0.79 | | 0.3528 | 2.0 | 226 | 0.6350 | 0.77 | | 0.178 | 3.0 | 339 | 0.7414 | 0.8 | | 0.142 | 4.0 | 452 | 0.5234 | 0.84 | | 0.1209 | 5.0 | 565 | 0.5176 | 0.88 | | 0.0004 | 6.0 | 678 | 0.4160 | 0.88 | | 0.0002 | 7.0 | 791 | 0.4798 | 0.9 | | 0.0002 | 8.0 | 904 | 0.4693 | 0.89 | | 0.1201 | 9.0 | 1017 | 0.4636 | 0.9 | | 0.0002 | 10.0 | 1130 | 0.4626 | 0.9 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

EleutherAI/pythia-2.8b-v0

EleutherAI

2023-07-10T01:35:41Z

1,819

5

transformers

[ "transformers", "pytorch", "safetensors", "gpt_neox", "text-generation", "causal-lm", "pythia", "pythia_v0", "en", "dataset:the_pile", "arxiv:2101.00027", "arxiv:2201.07311", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-11-20T03:56:10Z

--- language: - en tags: - pytorch - causal-lm - pythia - pythia_v0 license: apache-2.0 datasets: - the_pile --- The *Pythia Scaling Suite* is a collection of models developed to facilitate interpretability research. It contains two sets of eight models of sizes 70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two models: one trained on the Pile, and one trained on the Pile after the dataset has been globally deduplicated. All 8 model sizes are trained on the exact same data, in the exact same order. All Pythia models are available [on Hugging Face](https://huggingface.co/models?other=pythia). The Pythia model suite was deliberately designed to promote scientific research on large language models, especially interpretability research. Despite not centering downstream performance as a design goal, we find the models <a href="#evaluations">match or exceed</a> the performance of similar and same-sized models, such as those in the OPT and GPT-Neo suites. Please note that all models in the *Pythia* suite were renamed in January 2023. For clarity, a <a href="#naming-convention-and-parameter-count">table comparing the old and new names</a> is provided in this model card, together with exact parameter counts. ## Pythia-2.8B ### Model Details - Developed by: [EleutherAI](http://eleuther.ai) - Model type: Transformer-based Language Model - Language: English - Learn more: [Pythia's GitHub repository](https://github.com/EleutherAI/pythia) for training procedure, config files, and details on how to use. - Library: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) - License: Apache 2.0 - Contact: to ask questions about this model, join the [EleutherAI Discord](https://discord.gg/zBGx3azzUn), and post them in `#release-discussion`. Please read the existing *Pythia* documentation before asking about it in the EleutherAI Discord. For general correspondence: [contact@eleuther. ai](mailto:[email protected]). <figure> | Pythia model | Non-Embedding Params | Layers | Model Dim | Heads | Batch Size | Learning Rate | Equivalent Models | | -----------: | -------------------: | :----: | :-------: | :---: | :--------: | :-------------------: | :--------------------: | | 70M | 18,915,328 | 6 | 512 | 8 | 2M | 1.0 x 10-3 | — | | 160M | 85,056,000 | 12 | 768 | 12 | 4M | 6.0 x 10-4 | GPT-Neo 125M, OPT-125M | | 410M | 302,311,424 | 24 | 1024 | 16 | 4M | 3.0 x 10-4 | OPT-350M | | 1.0B | 805,736,448 | 16 | 2048 | 8 | 2M | 3.0 x 10-4 | — | | 1.4B | 1,208,602,624 | 24 | 2048 | 16 | 4M | 2.0 x 10-4 | GPT-Neo 1.3B, OPT-1.3B | | 2.8B | 2,517,652,480 | 32 | 2560 | 32 | 2M | 1.6 x 10-4 | GPT-Neo 2.7B, OPT-2.7B | | 6.9B | 6,444,163,072 | 32 | 4096 | 32 | 2M | 1.2 x 10-4 | OPT-6.7B | | 12B | 11,327,027,200 | 36 | 5120 | 40 | 2M | 1.2 x 10-4 | — | <figcaption>Engineering details for the Pythia Suite. Deduped and non-deduped models of a given size have the same hyperparameters. “Equivalent” models have exactly the same architecture, and the same number of non-embedding parameters.</figcaption> </figure> ### Uses and Limitations #### Intended Use The primary intended use of Pythia is research on the behavior, functionality, and limitations of large language models. This suite is intended to provide a controlled setting for performing scientific experiments. To enable the study of how language models change over the course of training, we provide 143 evenly spaced intermediate checkpoints per model. These checkpoints are hosted on Hugging Face as branches. Note that branch `143000` corresponds exactly to the model checkpoint on the `main` branch of each model. You may also further fine-tune and adapt Pythia-2.8B for deployment, as long as your use is in accordance with the Apache 2.0 license. Pythia models work with the Hugging Face [Transformers Library](https://huggingface.co/docs/transformers/index). If you decide to use pre-trained Pythia-2.8B as a basis for your fine-tuned model, please conduct your own risk and bias assessment. #### Out-of-scope use The Pythia Suite is **not** intended for deployment. It is not a in itself a product and cannot be used for human-facing interactions. Pythia models are English-language only, and are not suitable for translation or generating text in other languages. Pythia-2.8B has not been fine-tuned for downstream contexts in which language models are commonly deployed, such as writing genre prose, or commercial chatbots. This means Pythia-2.8B will **not** respond to a given prompt the way a product like ChatGPT does. This is because, unlike this model, ChatGPT was fine-tuned using methods such as Reinforcement Learning from Human Feedback (RLHF) to better “understand” human instructions. #### Limitations and biases The core functionality of a large language model is to take a string of text and predict the next token. The token deemed statistically most likely by the model need not produce the most “accurate” text. Never rely on Pythia-2.8B to produce factually accurate output. This model was trained on [the Pile](https://pile.eleuther.ai/), a dataset known to contain profanity and texts that are lewd or otherwise offensive. See [Section 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a discussion of documented biases with regards to gender, religion, and race. Pythia-2.8B may produce socially unacceptable or undesirable text, *even if* the prompt itself does not include anything explicitly offensive. If you plan on using text generated through, for example, the Hosted Inference API, we recommend having a human curate the outputs of this language model before presenting it to other people. Please inform your audience that the text was generated by Pythia-2.8B. ### Quickstart Pythia models can be loaded and used via the following code, demonstrated here for the third `pythia-70m-deduped` checkpoint: ```python from transformers import GPTNeoXForCausalLM, AutoTokenizer model = GPTNeoXForCausalLM.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) tokenizer = AutoTokenizer.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) inputs = tokenizer("Hello, I am", return_tensors="pt") tokens = model.generate(**inputs) tokenizer.decode(tokens[0]) ``` Revision/branch `step143000` corresponds exactly to the model checkpoint on the `main` branch of each model. For more information on how to use all Pythia models, see [documentation on GitHub](https://github.com/EleutherAI/pythia). ### Training #### Training data [The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in English. It was created by EleutherAI specifically for training large language models. It contains texts from 22 diverse sources, roughly broken down into five categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl), prose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and miscellaneous (e.g. GitHub, Enron Emails). See [the Pile paper](https://arxiv.org/abs/2101.00027) for a breakdown of all data sources, methodology, and a discussion of ethical implications. Consult [the datasheet](https://arxiv.org/abs/2201.07311) for more detailed documentation about the Pile and its component datasets. The Pile can be downloaded from the [official website](https://pile.eleuther.ai/), or from a [community mirror](https://the-eye.eu/public/AI/pile/). The Pile was **not** deduplicated before being used to train Pythia-2.8B. #### Training procedure All models were trained on the exact same data, in the exact same order. Each model saw 299,892,736,000 tokens during training, and 143 checkpoints for each model are saved every 2,097,152,000 tokens, spaced evenly throughout training. This corresponds to training for just under 1 epoch on the Pile for non-deduplicated models, and about 1.5 epochs on the deduplicated Pile. All *Pythia* models trained for the equivalent of 143000 steps at a batch size of 2,097,152 tokens. Two batch sizes were used: 2M and 4M. Models with a batch size of 4M tokens listed were originally trained for 71500 steps instead, with checkpoints every 500 steps. The checkpoints on Hugging Face are renamed for consistency with all 2M batch models, so `step1000` is the first checkpoint for `pythia-1.4b` that was saved (corresponding to step 500 in training), and `step1000` is likewise the first `pythia-6.9b` checkpoint that was saved (corresponding to 1000 “actual” steps). See [GitHub](https://github.com/EleutherAI/pythia) for more details on training procedure, including [how to reproduce it](https://github.com/EleutherAI/pythia/blob/main/README.md#reproducing-training). Pythia uses the same tokenizer as [GPT-NeoX- 20B](https://huggingface.co/EleutherAI/gpt-neox-20b). ### Evaluations All 16 *Pythia* models were evaluated using the [LM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness). You can access the results by model and step at `results/json/*` in the [GitHub repository](https://github.com/EleutherAI/pythia/tree/main/results/json). Expand the sections below to see plots of evaluation results for all Pythia and Pythia-deduped models compared with OPT and BLOOM. <details> <summary>LAMBADA – OpenAI</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/lambada_openai.png" style="width:auto"/> </details> <details> <summary>Physical Interaction: Question Answering (PIQA)</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/piqa.png" style="width:auto"/> </details> <details> <summary>WinoGrande</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/winogrande.png" style="width:auto"/> </details> <details> <summary>AI2 Reasoning Challenge—Challenge Set</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/arc_challenge.png" style="width:auto"/> </details> <details> <summary>SciQ</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/sciq.png" style="width:auto"/> </details> ### Naming convention and parameter count *Pythia* models were renamed in January 2023. It is possible that the old naming convention still persists in some documentation by accident. The current naming convention (70M, 160M, etc.) is based on total parameter count. <figure style="width:32em"> | current Pythia suffix | old suffix | total params | non-embedding params | | --------------------: | ---------: | -------------: | -------------------: | | 70M | 19M | 70,426,624 | 18,915,328 | | 160M | 125M | 162,322,944 | 85,056,000 | | 410M | 350M | 405,334,016 | 302,311,424 | | 1B | 800M | 1,011,781,632 | 805,736,448 | | 1.4B | 1.3B | 1,414,647,808 | 1,208,602,624 | | 2.8B | 2.7B | 2,775,208,960 | 2,517,652,480 | | 6.9B | 6.7B | 6,857,302,016 | 6,444,163,072 | | 12B | 13B | 11,846,072,320 | 11,327,027,200 | </figure>

EleutherAI/pythia-70m-deduped-v0

EleutherAI

2023-07-10T01:32:46Z

933

8

transformers

[ "transformers", "pytorch", "safetensors", "gpt_neox", "text-generation", "causal-lm", "pythia", "pythia_v0", "en", "dataset:EleutherAI/the_pile_deduplicated", "arxiv:2101.00027", "arxiv:2201.07311", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-11-01T00:24:53Z

--- language: - en tags: - pytorch - causal-lm - pythia - pythia_v0 license: apache-2.0 datasets: - EleutherAI/the_pile_deduplicated --- The *Pythia Scaling Suite* is a collection of models developed to facilitate interpretability research. It contains two sets of eight models of sizes 70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two models: one trained on the Pile, and one trained on the Pile after the dataset has been globally deduplicated. All 8 model sizes are trained on the exact same data, in the exact same order. All Pythia models are available [on Hugging Face](https://huggingface.co/models?other=pythia). The Pythia model suite was deliberately designed to promote scientific research on large language models, especially interpretability research. Despite not centering downstream performance as a design goal, we find the models <a href="#evaluations">match or exceed</a> the performance of similar and same-sized models, such as those in the OPT and GPT-Neo suites. Please note that all models in the *Pythia* suite were renamed in January 2023. For clarity, a <a href="#naming-convention-and-parameter-count">table comparing the old and new names</a> is provided in this model card, together with exact parameter counts. ## Pythia-70M-deduped ### Model Details - Developed by: [EleutherAI](http://eleuther.ai) - Model type: Transformer-based Language Model - Language: English - Learn more: [Pythia's GitHub repository](https://github.com/EleutherAI/pythia) for training procedure, config files, and details on how to use. - Library: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) - License: Apache 2.0 - Contact: to ask questions about this model, join the [EleutherAI Discord](https://discord.gg/zBGx3azzUn), and post them in `#release-discussion`. Please read the existing *Pythia* documentation before asking about it in the EleutherAI Discord. For general correspondence: [contact@eleuther. ai](mailto:[email protected]). <figure> | Pythia model | Non-Embedding Params | Layers | Model Dim | Heads | Batch Size | Learning Rate | Equivalent Models | | -----------: | -------------------: | :----: | :-------: | :---: | :--------: | :-------------------: | :--------------------: | | 70M | 18,915,328 | 6 | 512 | 8 | 2M | 1.0 x 10-3 | — | | 160M | 85,056,000 | 12 | 768 | 12 | 4M | 6.0 x 10-4 | GPT-Neo 125M, OPT-125M | | 410M | 302,311,424 | 24 | 1024 | 16 | 4M | 3.0 x 10-4 | OPT-350M | | 1.0B | 805,736,448 | 16 | 2048 | 8 | 2M | 3.0 x 10-4 | — | | 1.4B | 1,208,602,624 | 24 | 2048 | 16 | 4M | 2.0 x 10-4 | GPT-Neo 1.3B, OPT-1.3B | | 2.8B | 2,517,652,480 | 32 | 2560 | 32 | 2M | 1.6 x 10-4 | GPT-Neo 2.7B, OPT-2.7B | | 6.9B | 6,444,163,072 | 32 | 4096 | 32 | 2M | 1.2 x 10-4 | OPT-6.7B | | 12B | 11,327,027,200 | 36 | 5120 | 40 | 2M | 1.2 x 10-4 | — | <figcaption>Engineering details for the Pythia Suite. Deduped and non-deduped models of a given size have the same hyperparameters. “Equivalent” models have exactly the same architecture, and the same number of non-embedding parameters.</figcaption> </figure> ### Uses and Limitations #### Intended Use The primary intended use of Pythia is research on the behavior, functionality, and limitations of large language models. This suite is intended to provide a controlled setting for performing scientific experiments. To enable the study of how language models change in the course of training, we provide 143 evenly spaced intermediate checkpoints per model. These checkpoints are hosted on Hugging Face as branches. Note that branch `143000` corresponds exactly to the model checkpoint on the `main` branch of each model. You may also further fine-tune and adapt Pythia-70M-deduped for deployment, as long as your use is in accordance with the Apache 2.0 license. Pythia models work with the Hugging Face [Transformers Library](https://huggingface.co/docs/transformers/index). If you decide to use pre-trained Pythia-70M-deduped as a basis for your fine-tuned model, please conduct your own risk and bias assessment. #### Out-of-scope use The Pythia Suite is **not** intended for deployment. It is not a in itself a product and cannot be used for human-facing interactions. Pythia models are English-language only, and are not suitable for translation or generating text in other languages. Pythia-70M-deduped has not been fine-tuned for downstream contexts in which language models are commonly deployed, such as writing genre prose, or commercial chatbots. This means Pythia-70M-deduped will **not** respond to a given prompt the way a product like ChatGPT does. This is because, unlike this model, ChatGPT was fine-tuned using methods such as Reinforcement Learning from Human Feedback (RLHF) to better “understand” human instructions. #### Limitations and biases The core functionality of a large language model is to take a string of text and predict the next token. The token deemed statistically most likely by the model need not produce the most “accurate” text. Never rely on Pythia-70M-deduped to produce factually accurate output. This model was trained on [the Pile](https://pile.eleuther.ai/), a dataset known to contain profanity and texts that are lewd or otherwise offensive. See [Section 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a discussion of documented biases with regards to gender, religion, and race. Pythia-70M-deduped may produce socially unacceptable or undesirable text, *even if* the prompt itself does not include anything explicitly offensive. If you plan on using text generated through, for example, the Hosted Inference API, we recommend having a human curate the outputs of this language model before presenting it to other people. Please inform your audience that the text was generated by Pythia-70M-deduped. ### Quickstart Pythia models can be loaded and used via the following code, demonstrated here for the third `pythia-70m-deduped` checkpoint: ```python from transformers import GPTNeoXForCausalLM, AutoTokenizer model = GPTNeoXForCausalLM.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) tokenizer = AutoTokenizer.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) inputs = tokenizer("Hello, I am", return_tensors="pt") tokens = model.generate(**inputs) tokenizer.decode(tokens[0]) ``` Revision/branch `step143000` corresponds exactly to the model checkpoint on the `main` branch of each model. For more information on how to use all Pythia models, see [documentation on GitHub](https://github.com/EleutherAI/pythia). ### Training #### Training data Pythia-70M-deduped was trained on the Pile **after the dataset has been globally deduplicated**. [The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in English. It was created by EleutherAI specifically for training large language models. It contains texts from 22 diverse sources, roughly broken down into five categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl), prose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and miscellaneous (e.g. GitHub, Enron Emails). See [the Pile paper](https://arxiv.org/abs/2101.00027) for a breakdown of all data sources, methodology, and a discussion of ethical implications. Consult [the datasheet](https://arxiv.org/abs/2201.07311) for more detailed documentation about the Pile and its component datasets. The Pile can be downloaded from the [official website](https://pile.eleuther.ai/), or from a [community mirror](https://the-eye.eu/public/AI/pile/). #### Training procedure All models were trained on the exact same data, in the exact same order. Each model saw 299,892,736,000 tokens during training, and 143 checkpoints for each model are saved every 2,097,152,000 tokens, spaced evenly throughout training. This corresponds to training for just under 1 epoch on the Pile for non-deduplicated models, and about 1.5 epochs on the deduplicated Pile. All *Pythia* models trained for the equivalent of 143000 steps at a batch size of 2,097,152 tokens. Two batch sizes were used: 2M and 4M. Models with a batch size of 4M tokens listed were originally trained for 71500 steps instead, with checkpoints every 500 steps. The checkpoints on Hugging Face are renamed for consistency with all 2M batch models, so `step1000` is the first checkpoint for `pythia-1.4b` that was saved (corresponding to step 500 in training), and `step1000` is likewise the first `pythia-6.9b` checkpoint that was saved (corresponding to 1000 “actual” steps). See [GitHub](https://github.com/EleutherAI/pythia) for more details on training procedure, including [how to reproduce it](https://github.com/EleutherAI/pythia/blob/main/README.md#reproducing-training). Pythia uses the same tokenizer as [GPT-NeoX- 20B](https://huggingface.co/EleutherAI/gpt-neox-20b). ### Evaluations All 16 *Pythia* models were evaluated using the [LM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness). You can access the results by model and step at `results/json/*` in the [GitHub repository](https://github.com/EleutherAI/pythia/tree/main/results/json). Expand the sections below to see plots of evaluation results for all Pythia and Pythia-deduped models compared with OPT and BLOOM. <details> <summary>LAMBADA – OpenAI</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/lambada_openai.png" style="width:auto"/> </details> <details> <summary>Physical Interaction: Question Answering (PIQA)</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/piqa.png" style="width:auto"/> </details> <details> <summary>WinoGrande</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/winogrande.png" style="width:auto"/> </details> <details> <summary>AI2 Reasoning Challenge – Challenge Set</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/arc_challenge.png" style="width:auto"/> </details> <details> <summary>SciQ</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/sciq.png" style="width:auto"/> </details> ### Naming convention and parameter count *Pythia* models were renamed in January 2023. It is possible that the old naming convention still persists in some documentation by accident. The current naming convention (70M, 160M, etc.) is based on total parameter count. <figure style="width:32em"> | current Pythia suffix | old suffix | total params | non-embedding params | | --------------------: | ---------: | -------------: | -------------------: | | 70M | 19M | 70,426,624 | 18,915,328 | | 160M | 125M | 162,322,944 | 85,056,000 | | 410M | 350M | 405,334,016 | 302,311,424 | | 1B | 800M | 1,011,781,632 | 805,736,448 | | 1.4B | 1.3B | 1,414,647,808 | 1,208,602,624 | | 2.8B | 2.7B | 2,775,208,960 | 2,517,652,480 | | 6.9B | 6.7B | 6,857,302,016 | 6,444,163,072 | | 12B | 13B | 11,846,072,320 | 11,327,027,200 | </figure>

EleutherAI/pythia-410m-deduped-v0

EleutherAI

2023-07-10T01:31:39Z

860

6

transformers

[ "transformers", "pytorch", "safetensors", "gpt_neox", "text-generation", "causal-lm", "pythia", "pythia_v0", "en", "dataset:EleutherAI/the_pile_deduplicated", "arxiv:2101.00027", "arxiv:2201.07311", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-11-01T00:48:44Z

--- language: - en tags: - pytorch - causal-lm - pythia - pythia_v0 license: apache-2.0 datasets: - EleutherAI/the_pile_deduplicated --- The *Pythia Scaling Suite* is a collection of models developed to facilitate interpretability research. It contains two sets of eight models of sizes 70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two models: one trained on the Pile, and one trained on the Pile after the dataset has been globally deduplicated. All 8 model sizes are trained on the exact same data, in the exact same order. All Pythia models are available [on Hugging Face](https://huggingface.co/models?other=pythia). The Pythia model suite was deliberately designed to promote scientific research on large language models, especially interpretability research. Despite not centering downstream performance as a design goal, we find the models <a href="#evaluations">match or exceed</a> the performance of similar and same-sized models, such as those in the OPT and GPT-Neo suites. Please note that all models in the *Pythia* suite were renamed in January 2023. For clarity, a <a href="#naming-convention-and-parameter-count">table comparing the old and new names</a> is provided in this model card, together with exact parameter counts. ## Pythia-410M-deduped ### Model Details - Developed by: [EleutherAI](http://eleuther.ai) - Model type: Transformer-based Language Model - Language: English - Learn more: [Pythia's GitHub repository](https://github.com/EleutherAI/pythia) for training procedure, config files, and details on how to use. - Library: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) - License: Apache 2.0 - Contact: to ask questions about this model, join the [EleutherAI Discord](https://discord.gg/zBGx3azzUn), and post them in `#release-discussion`. Please read the existing *Pythia* documentation before asking about it in the EleutherAI Discord. For general correspondence: [contact@eleuther. ai](mailto:[email protected]). <figure> | Pythia model | Non-Embedding Params | Layers | Model Dim | Heads | Batch Size | Learning Rate | Equivalent Models | | -----------: | -------------------: | :----: | :-------: | :---: | :--------: | :-------------------: | :--------------------: | | 70M | 18,915,328 | 6 | 512 | 8 | 2M | 1.0 x 10-3 | — | | 160M | 85,056,000 | 12 | 768 | 12 | 4M | 6.0 x 10-4 | GPT-Neo 125M, OPT-125M | | 410M | 302,311,424 | 24 | 1024 | 16 | 4M | 3.0 x 10-4 | OPT-350M | | 1.0B | 805,736,448 | 16 | 2048 | 8 | 2M | 3.0 x 10-4 | — | | 1.4B | 1,208,602,624 | 24 | 2048 | 16 | 4M | 2.0 x 10-4 | GPT-Neo 1.3B, OPT-1.3B | | 2.8B | 2,517,652,480 | 32 | 2560 | 32 | 2M | 1.6 x 10-4 | GPT-Neo 2.7B, OPT-2.7B | | 6.9B | 6,444,163,072 | 32 | 4096 | 32 | 2M | 1.2 x 10-4 | OPT-6.7B | | 12B | 11,327,027,200 | 36 | 5120 | 40 | 2M | 1.2 x 10-4 | — | <figcaption>Engineering details for the Pythia Suite. Deduped and non-deduped models of a given size have the same hyperparameters. “Equivalent” models have exactly the same architecture, and the same number of non-embedding parameters.</figcaption> </figure> ### Uses and Limitations #### Intended Use The primary intended use of Pythia is research on the behavior, functionality, and limitations of large language models. This suite is intended to provide a controlled setting for performing scientific experiments. To enable the study of how language models change in the course of training, we provide 143 evenly spaced intermediate checkpoints per model. These checkpoints are hosted on Hugging Face as branches. Note that branch `143000` corresponds exactly to the model checkpoint on the `main` branch of each model. You may also further fine-tune and adapt Pythia-410M-deduped for deployment, as long as your use is in accordance with the Apache 2.0 license. Pythia models work with the Hugging Face [Transformers Library](https://huggingface.co/docs/transformers/index). If you decide to use pre-trained Pythia-410M-deduped as a basis for your fine-tuned model, please conduct your own risk and bias assessment. #### Out-of-scope use The Pythia Suite is **not** intended for deployment. It is not a in itself a product and cannot be used for human-facing interactions. Pythia models are English-language only, and are not suitable for translation or generating text in other languages. Pythia-410M-deduped has not been fine-tuned for downstream contexts in which language models are commonly deployed, such as writing genre prose, or commercial chatbots. This means Pythia-410M-deduped will **not** respond to a given prompt the way a product like ChatGPT does. This is because, unlike this model, ChatGPT was fine-tuned using methods such as Reinforcement Learning from Human Feedback (RLHF) to better “understand” human instructions. #### Limitations and biases The core functionality of a large language model is to take a string of text and predict the next token. The token deemed statistically most likely by the model need not produce the most “accurate” text. Never rely on Pythia-410M-deduped to produce factually accurate output. This model was trained on [the Pile](https://pile.eleuther.ai/), a dataset known to contain profanity and texts that are lewd or otherwise offensive. See [Section 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a discussion of documented biases with regards to gender, religion, and race. Pythia-410M-deduped may produce socially unacceptable or undesirable text, *even if* the prompt itself does not include anything explicitly offensive. If you plan on using text generated through, for example, the Hosted Inference API, we recommend having a human curate the outputs of this language model before presenting it to other people. Please inform your audience that the text was generated by Pythia-410M-deduped. ### Quickstart Pythia models can be loaded and used via the following code, demonstrated here for the third `pythia-70m-deduped` checkpoint: ```python from transformers import GPTNeoXForCausalLM, AutoTokenizer model = GPTNeoXForCausalLM.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) tokenizer = AutoTokenizer.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) inputs = tokenizer("Hello, I am", return_tensors="pt") tokens = model.generate(**inputs) tokenizer.decode(tokens[0]) ``` Revision/branch `step143000` corresponds exactly to the model checkpoint on the `main` branch of each model. For more information on how to use all Pythia models, see [documentation on GitHub](https://github.com/EleutherAI/pythia). ### Training #### Training data Pythia-410M-deduped was trained on the Pile **after the dataset has been globally deduplicated**. [The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in English. It was created by EleutherAI specifically for training large language models. It contains texts from 22 diverse sources, roughly broken down into five categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl), prose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and miscellaneous (e.g. GitHub, Enron Emails). See [the Pile paper](https://arxiv.org/abs/2101.00027) for a breakdown of all data sources, methodology, and a discussion of ethical implications. Consult [the datasheet](https://arxiv.org/abs/2201.07311) for more detailed documentation about the Pile and its component datasets. The Pile can be downloaded from the [official website](https://pile.eleuther.ai/), or from a [community mirror](https://the-eye.eu/public/AI/pile/). #### Training procedure All models were trained on the exact same data, in the exact same order. Each model saw 299,892,736,000 tokens during training, and 143 checkpoints for each model are saved every 2,097,152,000 tokens, spaced evenly throughout training. This corresponds to training for just under 1 epoch on the Pile for non-deduplicated models, and about 1.5 epochs on the deduplicated Pile. All *Pythia* models trained for the equivalent of 143000 steps at a batch size of 2,097,152 tokens. Two batch sizes were used: 2M and 4M. Models with a batch size of 4M tokens listed were originally trained for 71500 steps instead, with checkpoints every 500 steps. The checkpoints on Hugging Face are renamed for consistency with all 2M batch models, so `step1000` is the first checkpoint for `pythia-1.4b` that was saved (corresponding to step 500 in training), and `step1000` is likewise the first `pythia-6.9b` checkpoint that was saved (corresponding to 1000 “actual” steps). See [GitHub](https://github.com/EleutherAI/pythia) for more details on training procedure, including [how to reproduce it](https://github.com/EleutherAI/pythia/blob/main/README.md#reproducing-training). Pythia uses the same tokenizer as [GPT-NeoX- 20B](https://huggingface.co/EleutherAI/gpt-neox-20b). ### Evaluations All 16 *Pythia* models were evaluated using the [LM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness). You can access the results by model and step at `results/json/*` in the [GitHub repository](https://github.com/EleutherAI/pythia/tree/main/results/json). Expand the sections below to see plots of evaluation results for all Pythia and Pythia-deduped models compared with OPT and BLOOM. <details> <summary>LAMBADA – OpenAI</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/lambada_openai.png" style="width:auto"/> </details> <details> <summary>Physical Interaction: Question Answering (PIQA)</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/piqa.png" style="width:auto"/> </details> <details> <summary>WinoGrande</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/winogrande.png" style="width:auto"/> </details> <details> <summary>AI2 Reasoning Challenge – Challenge Set</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/arc_challenge.png" style="width:auto"/> </details> <details> <summary>SciQ</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/sciq.png" style="width:auto"/> </details> ### Naming convention and parameter count *Pythia* models were renamed in January 2023. It is possible that the old naming convention still persists in some documentation by accident. The current naming convention (70M, 160M, etc.) is based on total parameter count. <figure style="width:32em"> | current Pythia suffix | old suffix | total params | non-embedding params | | --------------------: | ---------: | -------------: | -------------------: | | 70M | 19M | 70,426,624 | 18,915,328 | | 160M | 125M | 162,322,944 | 85,056,000 | | 410M | 350M | 405,334,016 | 302,311,424 | | 1B | 800M | 1,011,781,632 | 805,736,448 | | 1.4B | 1.3B | 1,414,647,808 | 1,208,602,624 | | 2.8B | 2.7B | 2,775,208,960 | 2,517,652,480 | | 6.9B | 6.7B | 6,857,302,016 | 6,444,163,072 | | 12B | 13B | 11,846,072,320 | 11,327,027,200 | </figure>

EleutherAI/pythia-160m-deduped-v0

EleutherAI

2023-07-10T01:30:40Z

847

6

transformers

[ "transformers", "pytorch", "safetensors", "gpt_neox", "text-generation", "causal-lm", "pythia", "pythia_v0", "en", "dataset:EleutherAI/the_pile_deduplicated", "arxiv:2101.00027", "arxiv:2201.07311", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-10-18T02:59:41Z

--- language: - en tags: - pytorch - causal-lm - pythia - pythia_v0 license: apache-2.0 datasets: - EleutherAI/the_pile_deduplicated --- The *Pythia Scaling Suite* is a collection of models developed to facilitate interpretability research. It contains two sets of eight models of sizes 70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two models: one trained on the Pile, and one trained on the Pile after the dataset has been globally deduplicated. All 8 model sizes are trained on the exact same data, in the exact same order. All Pythia models are available [on Hugging Face](https://huggingface.co/models?other=pythia). The Pythia model suite was deliberately designed to promote scientific research on large language models, especially interpretability research. Despite not centering downstream performance as a design goal, we find the models <a href="#evaluations">match or exceed</a> the performance of similar and same-sized models, such as those in the OPT and GPT-Neo suites. Please note that all models in the *Pythia* suite were renamed in January 2023. For clarity, a <a href="#naming-convention-and-parameter-count">table comparing the old and new names</a> is provided in this model card, together with exact parameter counts. ## Pythia-160M-deduped ### Model Details - Developed by: [EleutherAI](http://eleuther.ai) - Model type: Transformer-based Language Model - Language: English - Learn more: [Pythia's GitHub repository](https://github.com/EleutherAI/pythia) for training procedure, config files, and details on how to use. - Library: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) - License: Apache 2.0 - Contact: to ask questions about this model, join the [EleutherAI Discord](https://discord.gg/zBGx3azzUn), and post them in `#release-discussion`. Please read the existing *Pythia* documentation before asking about it in the EleutherAI Discord. For general correspondence: [contact@eleuther. ai](mailto:[email protected]). <figure> | Pythia model | Non-Embedding Params | Layers | Model Dim | Heads | Batch Size | Learning Rate | Equivalent Models | | -----------: | -------------------: | :----: | :-------: | :---: | :--------: | :-------------------: | :--------------------: | | 70M | 18,915,328 | 6 | 512 | 8 | 2M | 1.0 x 10-3 | — | | 160M | 85,056,000 | 12 | 768 | 12 | 4M | 6.0 x 10-4 | GPT-Neo 125M, OPT-125M | | 410M | 302,311,424 | 24 | 1024 | 16 | 4M | 3.0 x 10-4 | OPT-350M | | 1.0B | 805,736,448 | 16 | 2048 | 8 | 2M | 3.0 x 10-4 | — | | 1.4B | 1,208,602,624 | 24 | 2048 | 16 | 4M | 2.0 x 10-4 | GPT-Neo 1.3B, OPT-1.3B | | 2.8B | 2,517,652,480 | 32 | 2560 | 32 | 2M | 1.6 x 10-4 | GPT-Neo 2.7B, OPT-2.7B | | 6.9B | 6,444,163,072 | 32 | 4096 | 32 | 2M | 1.2 x 10-4 | OPT-6.7B | | 12B | 11,327,027,200 | 36 | 5120 | 40 | 2M | 1.2 x 10-4 | — | <figcaption>Engineering details for the Pythia Suite. Deduped and non-deduped models of a given size have the same hyperparameters. “Equivalent” models have exactly the same architecture, and the same number of non-embedding parameters.</figcaption> </figure> ### Uses and Limitations #### Intended Use The primary intended use of Pythia is research on the behavior, functionality, and limitations of large language models. This suite is intended to provide a controlled setting for performing scientific experiments. To enable the study of how language models change in the course of training, we provide 143 evenly spaced intermediate checkpoints per model. These checkpoints are hosted on Hugging Face as branches. Note that branch `143000` corresponds exactly to the model checkpoint on the `main` branch of each model. You may also further fine-tune and adapt Pythia-160M-deduped for deployment, as long as your use is in accordance with the Apache 2.0 license. Pythia models work with the Hugging Face [Transformers Library](https://huggingface.co/docs/transformers/index). If you decide to use pre-trained Pythia-160M-deduped as a basis for your fine-tuned model, please conduct your own risk and bias assessment. #### Out-of-scope use The Pythia Suite is **not** intended for deployment. It is not a in itself a product and cannot be used for human-facing interactions. Pythia models are English-language only, and are not suitable for translation or generating text in other languages. Pythia-160M-deduped has not been fine-tuned for downstream contexts in which language models are commonly deployed, such as writing genre prose, or commercial chatbots. This means Pythia-160M-deduped will **not** respond to a given prompt the way a product like ChatGPT does. This is because, unlike this model, ChatGPT was fine-tuned using methods such as Reinforcement Learning from Human Feedback (RLHF) to better “understand” human instructions. #### Limitations and biases The core functionality of a large language model is to take a string of text and predict the next token. The token deemed statistically most likely by the model need not produce the most “accurate” text. Never rely on Pythia-160M-deduped to produce factually accurate output. This model was trained on [the Pile](https://pile.eleuther.ai/), a dataset known to contain profanity and texts that are lewd or otherwise offensive. See [Section 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a discussion of documented biases with regards to gender, religion, and race. Pythia-160M-deduped may produce socially unacceptable or undesirable text, *even if* the prompt itself does not include anything explicitly offensive. If you plan on using text generated through, for example, the Hosted Inference API, we recommend having a human curate the outputs of this language model before presenting it to other people. Please inform your audience that the text was generated by Pythia-160M-deduped. ### Quickstart Pythia models can be loaded and used via the following code, demonstrated here for the third `pythia-70m-deduped` checkpoint: ```python from transformers import GPTNeoXForCausalLM, AutoTokenizer model = GPTNeoXForCausalLM.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) tokenizer = AutoTokenizer.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) inputs = tokenizer("Hello, I am", return_tensors="pt") tokens = model.generate(**inputs) tokenizer.decode(tokens[0]) ``` Revision/branch `step143000` corresponds exactly to the model checkpoint on the `main` branch of each model. For more information on how to use all Pythia models, see [documentation on GitHub](https://github.com/EleutherAI/pythia). ### Training #### Training data Pythia-160M-deduped was trained on the Pile **after the dataset has been globally deduplicated**. [The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in English. It was created by EleutherAI specifically for training large language models. It contains texts from 22 diverse sources, roughly broken down into five categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl), prose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and miscellaneous (e.g. GitHub, Enron Emails). See [the Pile paper](https://arxiv.org/abs/2101.00027) for a breakdown of all data sources, methodology, and a discussion of ethical implications. Consult [the datasheet](https://arxiv.org/abs/2201.07311) for more detailed documentation about the Pile and its component datasets. The Pile can be downloaded from the [official website](https://pile.eleuther.ai/), or from a [community mirror](https://the-eye.eu/public/AI/pile/). #### Training procedure All models were trained on the exact same data, in the exact same order. Each model saw 299,892,736,000 tokens during training, and 143 checkpoints for each model are saved every 2,097,152,000 tokens, spaced evenly throughout training. This corresponds to training for just under 1 epoch on the Pile for non-deduplicated models, and about 1.5 epochs on the deduplicated Pile. All *Pythia* models trained for the equivalent of 143000 steps at a batch size of 2,097,152 tokens. Two batch sizes were used: 2M and 4M. Models with a batch size of 4M tokens listed were originally trained for 71500 steps instead, with checkpoints every 500 steps. The checkpoints on Hugging Face are renamed for consistency with all 2M batch models, so `step1000` is the first checkpoint for `pythia-1.4b` that was saved (corresponding to step 500 in training), and `step1000` is likewise the first `pythia-6.9b` checkpoint that was saved (corresponding to 1000 “actual” steps). See [GitHub](https://github.com/EleutherAI/pythia) for more details on training procedure, including [how to reproduce it](https://github.com/EleutherAI/pythia/blob/main/README.md#reproducing-training). Pythia uses the same tokenizer as [GPT-NeoX- 20B](https://huggingface.co/EleutherAI/gpt-neox-20b). ### Evaluations All 16 *Pythia* models were evaluated using the [LM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness). You can access the results by model and step at `results/json/*` in the [GitHub repository](https://github.com/EleutherAI/pythia/tree/main/results/json). Expand the sections below to see plots of evaluation results for all Pythia and Pythia-deduped models compared with OPT and BLOOM. <details> <summary>LAMBADA – OpenAI</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/lambada_openai.png" style="width:auto"/> </details> <details> <summary>Physical Interaction: Question Answering (PIQA)</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/piqa.png" style="width:auto"/> </details> <details> <summary>WinoGrande</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/winogrande.png" style="width:auto"/> </details> <details> <summary>AI2 Reasoning Challenge – Challenge Set</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/arc_challenge.png" style="width:auto"/> </details> <details> <summary>SciQ</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/sciq.png" style="width:auto"/> </details> ### Naming convention and parameter count *Pythia* models were renamed in January 2023. It is possible that the old naming convention still persists in some documentation by accident. The current naming convention (70M, 160M, etc.) is based on total parameter count. <figure style="width:32em"> | current Pythia suffix | old suffix | total params | non-embedding params | | --------------------: | ---------: | -------------: | -------------------: | | 70M | 19M | 70,426,624 | 18,915,328 | | 160M | 125M | 162,322,944 | 85,056,000 | | 410M | 350M | 405,334,016 | 302,311,424 | | 1B | 800M | 1,011,781,632 | 805,736,448 | | 1.4B | 1.3B | 1,414,647,808 | 1,208,602,624 | | 2.8B | 2.7B | 2,775,208,960 | 2,517,652,480 | | 6.9B | 6.7B | 6,857,302,016 | 6,444,163,072 | | 12B | 13B | 11,846,072,320 | 11,327,027,200 | </figure>

EleutherAI/pythia-6.9b-deduped-v0

EleutherAI

2023-07-10T01:30:05Z

833

20

transformers

[ "transformers", "pytorch", "gpt_neox", "text-generation", "causal-lm", "pythia", "pythia_v0", "en", "dataset:EleutherAI/the_pile_deduplicated", "arxiv:2101.00027", "arxiv:2201.07311", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-10-18T03:04:37Z

--- language: - en tags: - pytorch - causal-lm - pythia - pythia_v0 license: apache-2.0 datasets: - EleutherAI/the_pile_deduplicated --- The *Pythia Scaling Suite* is a collection of models developed to facilitate interpretability research. It contains two sets of eight models of sizes 70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two models: one trained on the Pile, and one trained on the Pile after the dataset has been globally deduplicated. All 8 model sizes are trained on the exact same data, in the exact same order. All Pythia models are available [on Hugging Face](https://huggingface.co/models?other=pythia). The Pythia model suite was deliberately designed to promote scientific research on large language models, especially interpretability research. Despite not centering downstream performance as a design goal, we find the models <a href="#evaluations">match or exceed</a> the performance of similar and same-sized models, such as those in the OPT and GPT-Neo suites. Please note that all models in the *Pythia* suite were renamed in January 2023. For clarity, a <a href="#naming-convention-and-parameter-count">table comparing the old and new names</a> is provided in this model card, together with exact parameter counts. ## Pythia-6.9B-deduped ### Model Details - Developed by: [EleutherAI](http://eleuther.ai) - Model type: Transformer-based Language Model - Language: English - Learn more: [Pythia's GitHub repository](https://github.com/EleutherAI/pythia) for training procedure, config files, and details on how to use. - Library: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) - License: Apache 2.0 - Contact: to ask questions about this model, join the [EleutherAI Discord](https://discord.gg/zBGx3azzUn), and post them in `#release-discussion`. Please read the existing *Pythia* documentation before asking about it in the EleutherAI Discord. For general correspondence: [contact@eleuther. ai](mailto:[email protected]). <figure> | Pythia model | Non-Embedding Params | Layers | Model Dim | Heads | Batch Size | Learning Rate | Equivalent Models | | -----------: | -------------------: | :----: | :-------: | :---: | :--------: | :-------------------: | :--------------------: | | 70M | 18,915,328 | 6 | 512 | 8 | 2M | 1.0 x 10-3 | — | | 160M | 85,056,000 | 12 | 768 | 12 | 4M | 6.0 x 10-4 | GPT-Neo 125M, OPT-125M | | 410M | 302,311,424 | 24 | 1024 | 16 | 4M | 3.0 x 10-4 | OPT-350M | | 1.0B | 805,736,448 | 16 | 2048 | 8 | 2M | 3.0 x 10-4 | — | | 1.4B | 1,208,602,624 | 24 | 2048 | 16 | 4M | 2.0 x 10-4 | GPT-Neo 1.3B, OPT-1.3B | | 2.8B | 2,517,652,480 | 32 | 2560 | 32 | 2M | 1.6 x 10-4 | GPT-Neo 2.7B, OPT-2.7B | | 6.9B | 6,444,163,072 | 32 | 4096 | 32 | 2M | 1.2 x 10-4 | OPT-6.7B | | 12B | 11,327,027,200 | 36 | 5120 | 40 | 2M | 1.2 x 10-4 | — | <figcaption>Engineering details for the Pythia Suite. Deduped and non-deduped models of a given size have the same hyperparameters. “Equivalent” models have exactly the same architecture, and the same number of non-embedding parameters.</figcaption> </figure> ### Uses and Limitations #### Intended Use The primary intended use of Pythia is research on the behavior, functionality, and limitations of large language models. This suite is intended to provide a controlled setting for performing scientific experiments. To enable the study of how language models change in the course of training, we provide 143 evenly spaced intermediate checkpoints per model. These checkpoints are hosted on Hugging Face as branches. Note that branch `143000` corresponds exactly to the model checkpoint on the `main` branch of each model. You may also further fine-tune and adapt Pythia-6.9B-deduped for deployment, as long as your use is in accordance with the Apache 2.0 license. Pythia models work with the Hugging Face [Transformers Library](https://huggingface.co/docs/transformers/index). If you decide to use pre-trained Pythia-6.9B-deduped as a basis for your fine-tuned model, please conduct your own risk and bias assessment. #### Out-of-scope use The Pythia Suite is **not** intended for deployment. It is not a in itself a product and cannot be used for human-facing interactions. Pythia models are English-language only, and are not suitable for translation or generating text in other languages. Pythia-6.9B-deduped has not been fine-tuned for downstream contexts in which language models are commonly deployed, such as writing genre prose, or commercial chatbots. This means Pythia-6.9B-deduped will **not** respond to a given prompt the way a product like ChatGPT does. This is because, unlike this model, ChatGPT was fine-tuned using methods such as Reinforcement Learning from Human Feedback (RLHF) to better “understand” human instructions. #### Limitations and biases The core functionality of a large language model is to take a string of text and predict the next token. The token deemed statistically most likely by the model need not produce the most “accurate” text. Never rely on Pythia-6.9B-deduped to produce factually accurate output. This model was trained on [the Pile](https://pile.eleuther.ai/), a dataset known to contain profanity and texts that are lewd or otherwise offensive. See [Section 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a discussion of documented biases with regards to gender, religion, and race. Pythia-6.9B-deduped may produce socially unacceptable or undesirable text, *even if* the prompt itself does not include anything explicitly offensive. If you plan on using text generated through, for example, the Hosted Inference API, we recommend having a human curate the outputs of this language model before presenting it to other people. Please inform your audience that the text was generated by Pythia-6.9B-deduped. ### Quickstart Pythia models can be loaded and used via the following code, demonstrated here for the third `pythia-70m-deduped` checkpoint: ```python from transformers import GPTNeoXForCausalLM, AutoTokenizer model = GPTNeoXForCausalLM.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) tokenizer = AutoTokenizer.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) inputs = tokenizer("Hello, I am", return_tensors="pt") tokens = model.generate(**inputs) tokenizer.decode(tokens[0]) ``` Revision/branch `step143000` corresponds exactly to the model checkpoint on the `main` branch of each model. For more information on how to use all Pythia models, see [documentation on GitHub](https://github.com/EleutherAI/pythia). ### Training #### Training data Pythia-6.9B-deduped was trained on the Pile **after the dataset has been globally deduplicated**. [The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in English. It was created by EleutherAI specifically for training large language models. It contains texts from 22 diverse sources, roughly broken down into five categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl), prose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and miscellaneous (e.g. GitHub, Enron Emails). See [the Pile paper](https://arxiv.org/abs/2101.00027) for a breakdown of all data sources, methodology, and a discussion of ethical implications. Consult [the datasheet](https://arxiv.org/abs/2201.07311) for more detailed documentation about the Pile and its component datasets. The Pile can be downloaded from the [official website](https://pile.eleuther.ai/), or from a [community mirror](https://the-eye.eu/public/AI/pile/). #### Training procedure All models were trained on the exact same data, in the exact same order. Each model saw 299,892,736,000 tokens during training, and 143 checkpoints for each model are saved every 2,097,152,000 tokens, spaced evenly throughout training. This corresponds to training for just under 1 epoch on the Pile for non-deduplicated models, and about 1.5 epochs on the deduplicated Pile. All *Pythia* models trained for the equivalent of 143000 steps at a batch size of 2,097,152 tokens. Two batch sizes were used: 2M and 4M. Models with a batch size of 4M tokens listed were originally trained for 71500 steps instead, with checkpoints every 500 steps. The checkpoints on Hugging Face are renamed for consistency with all 2M batch models, so `step1000` is the first checkpoint for `pythia-1.4b` that was saved (corresponding to step 500 in training), and `step1000` is likewise the first `pythia-6.9b` checkpoint that was saved (corresponding to 1000 “actual” steps). See [GitHub](https://github.com/EleutherAI/pythia) for more details on training procedure, including [how to reproduce it](https://github.com/EleutherAI/pythia/blob/main/README.md#reproducing-training). Pythia uses the same tokenizer as [GPT-NeoX- 20B](https://huggingface.co/EleutherAI/gpt-neox-20b). ### Evaluations All 16 *Pythia* models were evaluated using the [LM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness). You can access the results by model and step at `results/json/*` in the [GitHub repository](https://github.com/EleutherAI/pythia/tree/main/results/json). Expand the sections below to see plots of evaluation results for all Pythia and Pythia-deduped models compared with OPT and BLOOM. <details> <summary>LAMBADA – OpenAI</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/lambada_openai.png" style="width:auto"/> </details> <details> <summary>Physical Interaction: Question Answering (PIQA)</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/piqa.png" style="width:auto"/> </details> <details> <summary>WinoGrande</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/winogrande.png" style="width:auto"/> </details> <details> <summary>AI2 Reasoning Challenge – Challenge Set</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/arc_challenge.png" style="width:auto"/> </details> <details> <summary>SciQ</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/sciq.png" style="width:auto"/> </details> ### Naming convention and parameter count *Pythia* models were renamed in January 2023. It is possible that the old naming convention still persists in some documentation by accident. The current naming convention (70M, 160M, etc.) is based on total parameter count. <figure style="width:32em"> | current Pythia suffix | old suffix | total params | non-embedding params | | --------------------: | ---------: | -------------: | -------------------: | | 70M | 19M | 70,426,624 | 18,915,328 | | 160M | 125M | 162,322,944 | 85,056,000 | | 410M | 350M | 405,334,016 | 302,311,424 | | 1B | 800M | 1,011,781,632 | 805,736,448 | | 1.4B | 1.3B | 1,414,647,808 | 1,208,602,624 | | 2.8B | 2.7B | 2,775,208,960 | 2,517,652,480 | | 6.9B | 6.7B | 6,857,302,016 | 6,444,163,072 | | 13B | 12B | 11,846,072,320 | 11,327,027,200 | </figure>

havingAfish/CDA

havingAfish

2023-07-10T01:00:15Z

0

null

[ "dataset:movie_rationales", "region:us" ]

null

2023-07-10T00:59:26Z

--- datasets: - movie_rationales ---

crisU8/bert-finetuned-ner-clinical-trials-2

crisU8

2023-07-10T00:48:07Z

101

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "token-classification", "generated_from_trainer", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-07-10T00:29:03Z

--- license: cc-by-nc-4.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner-clinical-trials-2 results: [] ---  # bert-finetuned-ner-clinical-trials-2 This model is a fine-tuned version of [lcampillos/roberta-es-clinical-trials-ner](https://huggingface.co/lcampillos/roberta-es-clinical-trials-ner) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3516 - Precision: 0.7329 - Recall: 0.7755 - F1: 0.7536 - Accuracy: 0.9141 ## 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: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.158 | 1.0 | 502 | 0.3028 | 0.7104 | 0.7700 | 0.7390 | 0.9144 | | 0.1061 | 2.0 | 1004 | 0.3578 | 0.7020 | 0.7772 | 0.7377 | 0.9094 | | 0.123 | 3.0 | 1506 | 0.3296 | 0.7237 | 0.7750 | 0.7485 | 0.9139 | | 0.1007 | 4.0 | 2008 | 0.3516 | 0.7329 | 0.7755 | 0.7536 | 0.9141 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

LongshenOu/lyric-trans-en2zh

LongshenOu

2023-07-10T00:16:10Z

31

0

transformers

[ "transformers", "pytorch", "mbart", "license:cc-by-nc-sa-4.0", "endpoints_compatible", "region:us" ]

null

2023-07-10T00:07:39Z

--- license: cc-by-nc-sa-4.0 ---

NasimB/gpt2-concat-guten-rarity-all-mod-repetition-iorder-5k-p5k

NasimB

2023-07-10T00:11:09Z

3

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-09T22:15:33Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: gpt2-concat-guten-rarity-all-mod-repetition-iorder-5k-p5k results: [] ---  # gpt2-concat-guten-rarity-all-mod-repetition-iorder-5k-p5k This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 3.1812 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.7049 | 0.3 | 500 | 5.6332 | | 5.3603 | 0.59 | 1000 | 5.2033 | | 5.0063 | 0.89 | 1500 | 4.9509 | | 4.7286 | 1.18 | 2000 | 4.7987 | | 4.5752 | 1.48 | 2500 | 4.6728 | | 4.4634 | 1.78 | 3000 | 4.5663 | | 4.3226 | 2.07 | 3500 | 4.4933 | | 4.1472 | 2.37 | 4000 | 4.4458 | | 4.1157 | 2.67 | 4500 | 4.3824 | | 4.0756 | 2.96 | 5000 | 4.3282 | | 3.8402 | 3.26 | 5500 | 4.3258 | | 3.8183 | 3.55 | 6000 | 4.2905 | | 3.7968 | 3.85 | 6500 | 4.2597 | | 3.6538 | 4.15 | 7000 | 4.2640 | | 3.5239 | 4.44 | 7500 | 4.2506 | | 3.5235 | 4.74 | 8000 | 4.2375 | | 3.4943 | 5.04 | 8500 | 4.2350 | | 3.3327 | 5.33 | 9000 | 4.2405 | | 3.3319 | 5.63 | 9500 | 4.2383 | | 3.3325 | 5.92 | 10000 | 4.2378 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3