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ewqr2130/llama_sft_longer
ewqr2130
2024-01-31T18:11:42Z
739
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-01-31T18:05:26Z
--- license: apache-2.0 --- Model card Files and versions Community Settings No model card New: Create and edit this model card directly on the website! Downloads last month 0 Unable to determine this model's library. Check the docs .Model card Files and versions Community Settings No model card New: Create and edit this model card directly on the website! Downloads last month 0 Unable to determine this model's library. Check the docs .Model card Files and versions Community Settings No model card New: Create and edit this model card directly on the website! Downloads last month 0 Unable to determine this model's library. Check the docs .Model card Files and versions Community Settings No model card New: Create and edit this model card directly on the website! Downloads last month 0 Unable to determine this model's library. Check the docs .
sonthenguyen/OpenHermes-2.5-Mistral-7B-mt-bench-DPO
sonthenguyen
2024-02-04T02:04:30Z
739
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-02-02T15:50:58Z
--- license: apache-2.0 --- Training hyperparameters LoRA: r=16 lora_alpha=16 lora_dropout=0.05 bias="none" task_type="CAUSAL_LM" target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj'] Training arguments: auto_find_batch_size=True gradient_checkpointing=True learning_rate=5e-7 lr_scheduler_type="cosine" max_steps=3922 optim="paged_adamw_32bit" warmup_steps=100 DPOTrainer: beta=0.1 max_prompt_length=1024 max_length=1536
Danielbrdz/Barcenas-Orca-2-7b
Danielbrdz
2024-02-02T20:39:41Z
739
1
transformers
[ "transformers", "safetensors", "llama", "text-generation", "en", "es", "dataset:HuggingFaceH4/no_robots", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-02-02T18:10:13Z
--- license: other license_name: microsoft-research-license license_link: LICENSE datasets: - HuggingFaceH4/no_robots language: - en - es --- Model based on Microsoft Orca 2 7b. Trained with the HuggingFaceH4/no_robots dataset for better natural conversation. Symbolic model for me at least. Made with ❤️ in Guadalupe, Nuevo Leon, Mexico 🇲🇽
s3nh/Severusectum-7B-DPO
s3nh
2024-03-04T12:39:33Z
739
1
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "en", "base_model:FelixChao/Sectumsempra-7B-DPO", "base_model:FelixChao/WestSeverus-7B-DPO-v2", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-02-03T22:51:05Z
--- language: - en license: mit library_name: transformers tags: - mergekit - merge base_model: - FelixChao/Sectumsempra-7B-DPO - FelixChao/WestSeverus-7B-DPO-v2 pipeline_tag: text-generation model-index: - name: Severusectum-7B-DPO results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 71.5 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=s3nh/Severusectum-7B-DPO name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 88.55 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=s3nh/Severusectum-7B-DPO name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 64.79 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=s3nh/Severusectum-7B-DPO name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 72.45 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=s3nh/Severusectum-7B-DPO name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 83.27 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=s3nh/Severusectum-7B-DPO name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 70.51 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=s3nh/Severusectum-7B-DPO name: Open LLM Leaderboard --- ![image](severus.png) # Severusectum-7B-DPO This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [FelixChao/Sectumsempra-7B-DPO](https://huggingface.co/FelixChao/Sectumsempra-7B-DPO) * [FelixChao/WestSeverus-7B-DPO-v2](https://huggingface.co/FelixChao/WestSeverus-7B-DPO-v2) ### Configuration The following YAML configuration was used to produce this model: ```yaml base_model: FelixChao/Sectumsempra-7B-DPO dtype: bfloat16 merge_method: slerp parameters: t: - filter: self_attn value: [0.0, 0.5, 0.3, 0.7, 1.0] - filter: mlp value: [1.0, 0.5, 0.7, 0.3, 0.0] - value: 0.5 slices: - sources: - layer_range: [0, 32] model: FelixChao/Sectumsempra-7B-DPO - layer_range: [0, 32] model: FelixChao/WestSeverus-7B-DPO-v2 ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_s3nh__Severusectum-7B-DPO) | Metric |Value| |---------------------------------|----:| |Avg. |75.18| |AI2 Reasoning Challenge (25-Shot)|71.50| |HellaSwag (10-Shot) |88.55| |MMLU (5-Shot) |64.79| |TruthfulQA (0-shot) |72.45| |Winogrande (5-shot) |83.27| |GSM8k (5-shot) |70.51|
Xenon1/Xenon-3
Xenon1
2024-02-04T04:56:38Z
739
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "Xenon-3", "conversational", "en", "arxiv:2401.10020", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-02-04T04:52:26Z
--- language: - en license: apache-2.0 tags: - mistral - Xenon-3 pipeline_tag: text-generation --- # Model Card for Xenon-3 Mistral-7B-v0.1 model fine-tuned on the Ultrafeedback dataset using techinques shown in the paper [Self-Rewarding Language Models](https://arxiv.org/abs/2401.10020). ## Instruction format In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id. E.g. ``` text = "<s>[INST] What is your favourite condiment? [/INST]" "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> " "[INST] Do you have mayonnaise recipes? [/INST]" ``` This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method: ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained("Xenon1/Xenon-3") tokenizer = AutoTokenizer.from_pretrained("Xenon1/Xenon-3") messages = [ {"role": "user", "content": "What is your favourite condiment?"}, {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"}, {"role": "user", "content": "Do you have mayonnaise recipes?"} ] encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt") model_inputs = encodeds.to(device) model.to(device) generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True) decoded = tokenizer.batch_decode(generated_ids) print(decoded[0]) ``` ## Model Architecture This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices: - Grouped-Query Attention - Sliding-Window Attention - Byte-fallback BPE tokenizer
BioMistral/BioMistral-7B-TIES
BioMistral
2024-02-19T15:37:10Z
739
2
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "ties", "medical", "biology", "conversational", "fr", "en", "pl", "es", "it", "ro", "de", "nl", "dataset:pubmed", "arxiv:2306.01708", "arxiv:2402.10373", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "base_model:BioMistral/BioMistral-7B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-02-05T22:05:17Z
--- base_model: - mistralai/Mistral-7B-Instruct-v0.1 - BioMistral/BioMistral-7B library_name: transformers tags: - mergekit - merge - ties - medical - biology license: apache-2.0 datasets: - pubmed language: - fr - en - pl - es - it - ro - de - nl pipeline_tag: text-generation --- # BioMistral-7B-mistral7instruct-ties This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) as a base. ### Models Merged The following models were included in the merge: * [BioMistral/BioMistral-7B](https://huggingface.co/BioMistral/BioMistral-7B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: mistralai/Mistral-7B-Instruct-v0.1 - model: BioMistral/BioMistral-7B parameters: density: 0.5 weight: 0.5 merge_method: ties base_model: mistralai/Mistral-7B-Instruct-v0.1 parameters: normalize: true dtype: bfloat16 ``` <p align="center"> <img src="https://huggingface.co/BioMistral/BioMistral-7B/resolve/main/wordart_blue_m_rectangle.png?download=true" alt="drawing" width="250"/> </p> # BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains **Abstract:** Large Language Models (LLMs) have demonstrated remarkable versatility in recent years, offering potential applications across specialized domains such as healthcare and medicine. Despite the availability of various open-source LLMs tailored for health contexts, adapting general-purpose LLMs to the medical domain presents significant challenges. In this paper, we introduce BioMistral, an open-source LLM tailored for the biomedical domain, utilizing Mistral as its foundation model and further pre-trained on PubMed Central. We conduct a comprehensive evaluation of BioMistral on a benchmark comprising 10 established medical question-answering (QA) tasks in English. We also explore lightweight models obtained through quantization and model merging approaches. Our results demonstrate BioMistral's superior performance compared to existing open-source medical models and its competitive edge against proprietary counterparts. Finally, to address the limited availability of data beyond English and to assess the multilingual generalization of medical LLMs, we automatically translated and evaluated this benchmark into 7 other languages. This marks the first large-scale multilingual evaluation of LLMs in the medical domain. Datasets, multilingual evaluation benchmarks, scripts, and all the models obtained during our experiments are freely released. **Advisory Notice!** Although BioMistral is intended to encapsulate medical knowledge sourced from high-quality evidence, it hasn't been tailored to effectively, safely, or suitably convey this knowledge within professional parameters for action. We advise refraining from utilizing BioMistral in medical contexts unless it undergoes thorough alignment with specific use cases and undergoes further testing, notably including randomized controlled trials in real-world medical environments. BioMistral 7B may possess inherent risks and biases that have not yet been thoroughly assessed. Additionally, the model's performance has not been evaluated in real-world clinical settings. Consequently, we recommend using BioMistral 7B strictly as a research tool and advise against deploying it in production environments for natural language generation or any professional health and medical purposes. # 1. BioMistral models **BioMistral** is a suite of Mistral-based further pre-trained open source models suited for the medical domains and pre-trained using textual data from PubMed Central Open Access (CC0, CC BY, CC BY-SA, and CC BY-ND). All the models are trained using the CNRS (French National Centre for Scientific Research) [Jean Zay](http://www.idris.fr/jean-zay/) French HPC. | Model Name | Base Model | Model Type | Sequence Length | Download | |:-------------------:|:----------------------------------:|:-------------------:|:---------------:|:-----------------------------------------------------:| | BioMistral-7B | [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) | Further Pre-trained | 2048 | [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B) | | BioMistral-7B-DARE | [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) | Merge DARE | 2048 | [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-DARE) | | BioMistral-7B-TIES | [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) | Merge TIES | 2048 | [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-TIES) | | BioMistral-7B-SLERP | [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) | Merge SLERP | 2048 | [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-SLERP) | # 2. Quantized Models | Base Model | Method | q_group_size | w_bit | version | VRAM GB | Time | Download | |:-------------------:|:------:|:------------:|:-----:|:-------:|:-------:|:------:|:--------:| | BioMistral-7B | FP16/BF16 | | | | 15.02 | x1.00 | [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B) | | BioMistral-7B | AWQ | 128 | 4 | GEMM | 4.68 | x1.41 | [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-AWQ-QGS128-W4-GEMM) | | BioMistral-7B | AWQ | 128 | 4 | GEMV | 4.68 | x10.30 | [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-AWQ-QGS128-W4-GEMV) | | BioMistral-7B | BnB.4 | | 4 | | 5.03 | x3.25 | [HuggingFace](blank) | | BioMistral-7B | BnB.8 | | 8 | | 8.04 | x4.34 | [HuggingFace](blank) | | BioMistral-7B-DARE | AWQ | 128 | 4 | GEMM | 4.68 | x1.41 | [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-DARE-AWQ-QGS128-W4-GEMM) | | BioMistral-7B-TIES | AWQ | 128 | 4 | GEMM | 4.68 | x1.41 | [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-TIES-AWQ-QGS128-W4-GEMM) | | BioMistral-7B-SLERP | AWQ | 128 | 4 | GEMM | 4.68 | x1.41 | [HuggingFace](https://huggingface.co/BioMistral/BioMistral-7B-SLERP-AWQ-QGS128-W4-GEMM) | # 2. Using BioMistral You can use BioMistral with [Hugging Face's Transformers library](https://github.com/huggingface/transformers) as follow. Loading the model and tokenizer : ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("BioMistral/BioMistral-7B") model = AutoModel.from_pretrained("BioMistral/BioMistral-7B") ``` # 3. Supervised Fine-tuning Benchmark | | Clinical KG | Medical Genetics | Anatomy | Pro Medicine | College Biology | College Medicine | MedQA | MedQA 5 opts | PubMedQA | MedMCQA | Avg. | |-------------------------------------------|:---------------------------------------------:|-----------------------------------------------|-----------------------------------------------|-----------------------------------------------|-----------------------------------------------|-----------------------------------------------|-----------------------------------------------|-----------------------------------------------|-----------------------------------------------|-----------------------------------------------|------------------| | **BioMistral 7B** | 59.9 | 64.0 | 56.5 | 60.4 | 59.0 | 54.7 | 50.6 | 42.8 | 77.5 | 48.1 | 57.3 | | **Mistral 7B Instruct** | **62.9** | 57.0 | 55.6 | 59.4 | 62.5 | <u>57.2</u> | 42.0 | 40.9 | 75.7 | 46.1 | 55.9 | | | | | | | | | | | | | | | **BioMistral 7B Ensemble** | <u>62.8</u> | 62.7 | <u>57.5</u> | **63.5** | 64.3 | 55.7 | 50.6 | 43.6 | 77.5 | **48.8** | 58.7 | | **BioMistral 7B DARE** | 62.3 | **67.0** | 55.8 | 61.4 | **66.9** | **58.0** | **51.1** | **45.2** | <u>77.7</u> | <u>48.7</u> | **59.4** | | **BioMistral 7B TIES** | 60.1 | <u>65.0</u> | **58.5** | 60.5 | 60.4 | 56.5 | 49.5 | 43.2 | 77.5 | 48.1 | 57.9 | | **BioMistral 7B SLERP** | 62.5 | 64.7 | 55.8 | <u>62.7</u> | <u>64.8</u> | 56.3 | <u>50.8</u> | <u>44.3</u> | **77.8** | 48.6 | <u>58.8</u> | | | | | | | | | | | | | | | **MedAlpaca 7B** | 53.1 | 58.0 | 54.1 | 58.8 | 58.1 | 48.6 | 40.1 | 33.7 | 73.6 | 37.0 | 51.5 | | **PMC-LLaMA 7B** | 24.5 | 27.7 | 35.3 | 17.4 | 30.3 | 23.3 | 25.5 | 20.2 | 72.9 | 26.6 | 30.4 | | **MediTron-7B** | 41.6 | 50.3 | 46.4 | 27.9 | 44.4 | 30.8 | 41.6 | 28.1 | 74.9 | 41.3 | 42.7 | | **BioMedGPT-LM-7B** | 51.4 | 52.0 | 49.4 | 53.3 | 50.7 | 49.1 | 42.5 | 33.9 | 76.8 | 37.6 | 49.7 | | | | | | | | | | | | | | | **GPT-3.5 Turbo 1106*** | 74.71 | 74.00 | 65.92 | 72.79 | 72.91 | 64.73 | 57.71 | 50.82 | 72.66 | 53.79 | 66.0 | Supervised Fine-Tuning (SFT) performance of BioMistral 7B models compared to baselines, measured by accuracy (↑) and averaged across 3 random seeds of 3-shot. DARE, TIES, and SLERP are model merging strategies that combine BioMistral 7B and Mistral 7B Instruct. Best model in bold, and second-best underlined. *GPT-3.5 Turbo performances are reported from the 3-shot results without SFT. # Citation BibTeX Arxiv : [https://arxiv.org/abs/2402.10373](https://arxiv.org/abs/2402.10373) ```bibtex @misc{labrak2024biomistral, title={BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains}, author={Yanis Labrak and Adrien Bazoge and Emmanuel Morin and Pierre-Antoine Gourraud and Mickael Rouvier and Richard Dufour}, year={2024}, eprint={2402.10373}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` **CAUTION!** Both direct and downstream users need to be informed about the risks, biases, and constraints inherent in the model. While the model can produce natural language text, our exploration of its capabilities and limitations is just beginning. In fields such as medicine, comprehending these limitations is crucial. Hence, we strongly advise against deploying this model for natural language generation in production or for professional tasks in the realm of health and medicine.
abhinand/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft
abhinand
2024-03-04T12:55:57Z
739
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "en", "dataset:teknium/OpenHermes-2.5", "dataset:abhinand/ultrachat_200k_sharegpt", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-02-06T19:00:10Z
--- language: - en license: apache-2.0 datasets: - teknium/OpenHermes-2.5 - abhinand/ultrachat_200k_sharegpt model-index: - name: TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 33.79 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abhinand/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 58.72 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abhinand/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 24.52 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abhinand/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 36.22 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abhinand/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 60.93 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abhinand/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 5.38 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abhinand/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft name: Open LLM Leaderboard --- # TinyLLaMA OpenHermes2.5 [Work in Progress] This a finetune of TinyLLaMA base model finetuned on [OpenHermes 2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5) and [UltraChat 200k](https://huggingface.co/datasets/abhinand/ultrachat_200k_sharegpt) for a single epoch. Training was generously supported by [Jarvislabs.ai](https://jarvislabs.ai/). If you appreciate this work and would like to support its continued development, consider [buying me a coffee](https://www.buymeacoffee.com/abhinand.b). Your support is invaluable and greatly appreciated. [!["Buy Me A Coffee"](https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png)](https://www.buymeacoffee.com/abhinand.b) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer trust_remote_code: true is_llama_derived_model: true # huggingface repo datasets: - path: teknium/OpenHermes-2.5 type: sharegpt conversation: chatml train_on_split: train - path: abhinand/ultrachat_200k_sharegpt type: sharegpt conversation: chatml train_on_split: train load_in_4bit: false load_in_8bit: false bf16: true # require >=ampere chat_template: chatml dataset_prepared_path: last_run_prepared_path hub_model_id: abhinand/TinyLlama-1.1B-OpenHermes-2.5-Chat-v1.0 group_by_length: false val_set_size: 0.0 sequence_len: 2048 sample_packing: true pad_to_sequence_len: true adapter: lora lora_model_dir: lora_r: 32 lora_alpha: 16 lora_target_modules: - q_proj - v_proj - k_proj - o_proj - gate_proj - down_proj - up_proj lora_modules_to_save: - embed_tokens - lm_head lora_dropout: 0.05 lora_target_linear: true lora_fan_in_fan_out: output_dir: /home/tiny-llama/trained_models gradient_accumulation_steps: 2 micro_batch_size: 32 eval_batch_size: 32 num_epochs: 1 logging_steps: 1 save_steps: 50 save_total_limit: 3 save_safetensors: true gradient_checkpointing: true lr_scheduler: cosine optimizer: "adamw_bnb_8bit" adam_beta2: 0.95 adam_epsilon: 0.00001 weight_decay: 0.1 learning_rate: 0.0005 max_grad_norm: 1.0 warmup_ratio: 0.05 # warmup_steps: 100 flash_attention: true # Resume from a specific checkpoint dir resume_from_checkpoint: # If resume_from_checkpoint isn't set and you simply want it to start where it left off. # Be careful with this being turned on between different models. # auto_resume_from_checkpoints: true # wandb configuration if you're using it # Make sure your `WANDB_API_KEY` environment variable is set (recommended) or you login to wandb with `wandb login`. wandb_mode: # "offline" to save run metadata locally and not sync to the server, "disabled" to turn off wandb wandb_project: "tiny-llama-sft" wandb_name: wandb_run_id: special_tokens: bos_token: "<s>" eos_token: "</s>" unk_token: "<unk>" tokens: # these are delimiters - "<|im_start|>" - "<|im_end|>" ``` </details> ## 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 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 476 - num_epochs: 1 ### Framework versions - PEFT 0.8.2 - Transformers 4.38.0.dev0 - Pytorch 2.0.1 - Datasets 2.16.1 - Tokenizers 0.15.0 # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_abhinand__TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft) | Metric |Value| |---------------------------------|----:| |Avg. |36.59| |AI2 Reasoning Challenge (25-Shot)|33.79| |HellaSwag (10-Shot) |58.72| |MMLU (5-Shot) |24.52| |TruthfulQA (0-shot) |36.22| |Winogrande (5-shot) |60.93| |GSM8k (5-shot) | 5.38|
ChuckMcSneed/Gembo-v1.1-70b
ChuckMcSneed
2024-03-04T13:43:45Z
739
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "nsfw", "not-for-all-audiences", "en", "ru", "license:llama2", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-02-10T02:07:32Z
--- language: - en - ru license: llama2 tags: - merge - mergekit - nsfw - not-for-all-audiences model-index: - name: Gembo-v1.1-70b results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 70.99 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ChuckMcSneed/Gembo-v1.1-70b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 86.9 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ChuckMcSneed/Gembo-v1.1-70b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 70.63 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ChuckMcSneed/Gembo-v1.1-70b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 62.45 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ChuckMcSneed/Gembo-v1.1-70b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 80.51 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ChuckMcSneed/Gembo-v1.1-70b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 50.64 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ChuckMcSneed/Gembo-v1.1-70b name: Open LLM Leaderboard --- ![logo-gembo-1.1.png](logo-gembo-1.1.png) This is like [Gembo v1](https://huggingface.co/ChuckMcSneed/Gembo-v1-70b), but with 6-7% more human data. Does perform a bit worse on the benches(who cares? I do.), but should be able to write in more diverse styles(See [waxwing-styles.txt](waxwing-styles.txt), tested it with v1, v1 does it better.). Mainly made for RP, but should be okay as an assistant. Turned out quite good, considering the amount of LORAs I merged into it. # Observations - GPTisms and repetition: put temperature and rep. pen. higher, make GPTisms stop sequences - A bit different than the ususal stuff; I'd say that it has so much slop in it that it unslops itself - Lightly censored - Fairly neutral, can be violent if you ask it really good, Goliath is a bit better at it - Has a bit of optimism baked in, but it's not very severe, maybe a tiny bit more than in v1? - Don't put too many style tags, here less is better - Unlike v1, 1.1 knows a bit better when to stop - Needs more wrangling than v1, but once you get it going it's good - Sometimes can't handle ' - Moderately intelligent - Quite creative # Worth over v1? Nah. I prefer hyperslop over this "humanized" one. Maybe I've been poisoned by slop. # Naming Internal name of this model was euryale-guano-saiga-med-janboros-kim-wing-lima-wiz-tony-d30-s40, but I decided to keep it short, and since it was iteration G in my files, I called it "Gembo". # Prompt format Alpaca. You can also try some other formats, I'm pretty sure it has a lot of them from all those merges. ``` ### Instruction: {instruction} ### Response: ``` # Settings As I already mentioned, high temperature and rep.pen. works great. For RP try something like this: - temperature=5 - MinP=0.10 - rep.pen.=1.15 Adjust to match your needs. # How it was created I took Sao10K/Euryale-1.3-L2-70B (Good base model) and added - Mikael110/llama-2-70b-guanaco-qlora (Creativity+assistant) - IlyaGusev/saiga2_70b_lora (Creativity+assistant) - s1ghhh/medllama-2-70b-qlora-1.1 (More data) - v2ray/Airoboros-2.1-Jannie-70B-QLoRA (Creativity+assistant) - Chat-Error/fiction.live-Kimiko-V2-70B (Creativity) - alac/Waxwing-Storytelling-70B-LoRA (New, creativity) - Doctor-Shotgun/limarpv3-llama2-70b-qlora (Creativity) - v2ray/LLaMA-2-Wizard-70B-QLoRA (Creativity+assistant) - v2ray/TonyGPT-70B-QLoRA (Special spice) Then I SLERP-merged it with cognitivecomputations/dolphin-2.2-70b (Needed to bridge the gap between this wonderful mess and Smaxxxer, otherwise it's quality is low) with 0.3t and then SLERP-merged it again with ChuckMcSneed/SMaxxxer-v1-70b (Creativity) with 0.4t. For SLERP-merges I used https://github.com/arcee-ai/mergekit. # Benchmarks (Do they even mean anything anymore?) ### NeoEvalPlusN_benchmark [My meme benchmark.](https://huggingface.co/datasets/ChuckMcSneed/NeoEvalPlusN_benchmark) | Test name | Gembo | Gembo 1.1 | | ---------- | ---------- | ---------- | | B | 2.5 | 2.5 | | C | 1.5 | 1.5 | | D | 3 | 3 | | S | 7.5 | 6.75 | | P | 5.25 | 5.25 | | Total | 19.75 | 19 | ### [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) [Leaderboard on Huggingface](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |Model |Average|ARC |HellaSwag|MMLU |TruthfulQA|Winogrande|GSM8K| |--------------|-------|-----|---------|-----|----------|----------|-----| |Gembo-v1-70b |70.51 |71.25|86.98 |70.85|63.25 |80.51 |50.19| |Gembo-v1.1-70b|70.35 |70.99|86.9 |70.63|62.45 |80.51 |50.64| Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ChuckMcSneed__Gembo-v1.1-70b) | Metric |Value| |---------------------------------|----:| |Avg. |70.35| |AI2 Reasoning Challenge (25-Shot)|70.99| |HellaSwag (10-Shot) |86.90| |MMLU (5-Shot) |70.63| |TruthfulQA (0-shot) |62.45| |Winogrande (5-shot) |80.51| |GSM8k (5-shot) |50.64|
vicgalle/Mixtral-7Bx2-truthy
vicgalle
2024-03-04T12:16:59Z
739
2
transformers
[ "transformers", "safetensors", "mixtral", "text-generation", "conversational", "dataset:jondurbin/truthy-dpo-v0.1", "arxiv:1910.09700", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-02-10T18:16:53Z
--- license: apache-2.0 library_name: transformers datasets: - jondurbin/truthy-dpo-v0.1 model-index: - name: Mixtral-7Bx2-truthy results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 72.18 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/Mixtral-7Bx2-truthy name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 87.88 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/Mixtral-7Bx2-truthy name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 65.2 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/Mixtral-7Bx2-truthy name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 74.68 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/Mixtral-7Bx2-truthy name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 80.66 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/Mixtral-7Bx2-truthy name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 67.25 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/Mixtral-7Bx2-truthy name: Open LLM Leaderboard --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ``` "results": { "truthfulqa_mc": { "mc1": 0.6107711138310894, "mc1_stderr": 0.017068552680690338, "mc2": 0.7527999957012117, "mc2_stderr": 0.014045181780156504 } ``` ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_vicgalle__Mixtral-7Bx2-truthy) | Metric |Value| |---------------------------------|----:| |Avg. |74.64| |AI2 Reasoning Challenge (25-Shot)|72.18| |HellaSwag (10-Shot) |87.88| |MMLU (5-Shot) |65.20| |TruthfulQA (0-shot) |74.68| |Winogrande (5-shot) |80.66| |GSM8k (5-shot) |67.25|
yam-peleg/Experiment4-7B
yam-peleg
2024-02-11T11:30:38Z
739
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-02-11T10:29:57Z
--- library_name: transformers license: apache-2.0 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
liminerity/Omningotex-7b-slerp
liminerity
2024-03-11T16:12:46Z
739
3
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "liminerity/binarized-ingotrix-slerp-7b", "eren23/dpo-binarized-NeutrixOmnibe-7B", "base_model:liminerity/binarized-ingotrix-slerp-7b", "base_model:eren23/dpo-binarized-NeutrixOmnibe-7B", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-02-12T10:34:11Z
--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - liminerity/binarized-ingotrix-slerp-7b - eren23/dpo-binarized-NeutrixOmnibe-7B base_model: - liminerity/binarized-ingotrix-slerp-7b - eren23/dpo-binarized-NeutrixOmnibe-7B model-index: - name: Omningotex-7b-slerp results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 73.29 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/Omningotex-7b-slerp name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 88.96 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/Omningotex-7b-slerp name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 64.69 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/Omningotex-7b-slerp name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 76.32 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/Omningotex-7b-slerp name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 84.21 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/Omningotex-7b-slerp name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 70.51 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/Omningotex-7b-slerp name: Open LLM Leaderboard --- Title: Introducing Omningotex-7b: The World's Most Accurate 7B LLM Today, I'm excited to share the creation of a groundbreaking language model, "liminerity/Omningotex-7b-slerp." This model has achieved an impressive accuracy rate of 76.33%, making it the most accurate 7B LLM in the world. The journey to create Omningotex-7b-slerp began with an experimental process called "merging." I started with a model named "ingot-7b-slerp," which was created by merging two other LLMs, "blurred-beagle-7b-slerp" (by myself, liminerity) and "Macaroni-7b-Tied" (by andrijdavid), a total of eight times over. After the successful creation of ingot-7b-slerp, I proceeded to merge it with another model, "dpo-binarized-NeuralTrix-7B" by eren23, using gradient slerp. The resulting model, "binarized-ingotrix-slerp-7b," achieved an accuracy rate of 76.04%. To further enhance the model's performance, I decided to merge "binarized-ingotrix-slerp-7b" with "dpo-binarized-NeutrixOmnibe-7B" by eren23 once again. The resulting model, "Omningotex-7b," is now the most accurate 7B LLM available. This breakthrough in LLM accuracy was achieved through a combination of careful experimentation and a deep understanding of the underlying algorithms and techniques. I believe that Omningotex-7b-slerp's success demonstrates the potential for further advancements in the field of natural language processing and artificial intelligence. I look forward to sharing more updates and insights as I continue to explore the possibilities of LLMs and push the boundaries of what is possible in the world of AI. Stay tuned for more exciting developments in the future! A huge thank you to Maxime Labonne and his creation of LazyMergeKit colab project. Use of it helped me gain a further grasp of the concepts at play and led to the creation of this model. I'm sure it won't be number 1 for long which excited me even more! Next, I set out to learn how to fine-tune with the resources I have available. My next overall goal is to try and find a way to produce a smaller model with high accuracy either through merging down using fewer layers after each merge. I may need to include finetuning between each merge or merging larger more accurate models into a smaller base while maintaining accuracy and performance. Every version of "TinyMistral" I come by seems to be bricked in the sense it spits out nonsense. Thank you for your time If you read this all the way. # Omningotex-7B-slerp Omningotex-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [liminerity/binarized-ingotrix-slerp-7b](https://huggingface.co/liminerity/binarized-ingotrix-slerp-7b) * [eren23/dpo-binarized-NeutrixOmnibe-7B](https://huggingface.co/eren23/dpo-binarized-NeutrixOmnibe-7B) ## 🧩 Configuration ```yaml slices: - sources: - model: liminerity/binarized-ingotrix-slerp-7b layer_range: [0, 32] - model: eren23/dpo-binarized-NeutrixOmnibe-7B layer_range: [0, 32] merge_method: slerp base_model: liminerity/binarized-ingotrix-slerp-7b parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "liminerity/Omningotex-7b-slerp" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_liminerity__Omningotex-7b-slerp) | Metric |Value| |---------------------------------|----:| |Avg. |76.33| |AI2 Reasoning Challenge (25-Shot)|73.29| |HellaSwag (10-Shot) |88.96| |MMLU (5-Shot) |64.69| |TruthfulQA (0-shot) |76.32| |Winogrande (5-shot) |84.21| |GSM8k (5-shot) |70.51|
ArianAskari/SOLID-SFT-DPO-MixQV2-SOLIDRejected-SFTChosen-Zephyr-7b-beta
ArianAskari
2024-02-13T06:21:46Z
739
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "en", "dataset:ArianAskari/SOLID", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-02-13T00:16:03Z
--- library_name: transformers tags: [] license: apache-2.0 language: - en datasets: ArianAskari/SOLID --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
mzio/hedgehog-mistral_7b-alpaca_clean-smd_lora_1e_3
mzio
2024-02-14T09:03:12Z
739
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "dataset:yahma/alpaca-cleaned", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-02-14T08:49:46Z
--- license: apache-2.0 datasets: - yahma/alpaca-cleaned --- # 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 Configs ``` name: llama model: pretrained_model_name_or_path: 'mistralai/Mistral-7B-v0.1' cache_dir: '/juice/scr/scr110/scr/nlp/data/neo/hub/' return_dict: true quantization: false device_map: auto # null low_cpu_mem_usage: true # false torch_dtype: bfloat16 attn_implementation: eager # so we can load attention weights rope_theta: 10000.0 attention: attention_type: hedgehog_llama feature_map: softmax_dim feature_map_kwargs: input_dim: 128 eps: 1e-12 # mlp: null # to set fullspace: true layer_idx: null # to set learned_kernel: untied_head learned_kernel_kwargs: feature_dim: 128 skip_connection: false bias: false zero_init: false tie_qk_kernels: false train_qk: true peft: method: lora kwargs: r: 8 # 256 lora_alpha: 16 # 512 lora_dropout: 0.1 # 0.05 target_modules: ['self_attn.q_proj', 'self_attn.k_proj'] dataset: name: alpaca_clean dataset_config: name: alpaca path: yahma/alpaca-cleaned chunk_size: 1024 # 2048 concat_data: true cache_dir: '/u/scr/nlp/data/alpaca' pretrained_model_config: pretrained_model_name_or_path: 'mistralai/Mistral-7B-v0.1' cache_dir: '/juice/scr/scr110/scr/nlp/data/neo/hub/' preprocess_config: null dataloader: batch_size: 1 num_workers: 2 drop_last: false pin_memory: true optimizer: optim: adamw_torch_fused lr: 0.001 weight_decay: 0.0 lr_scheduler: lr_scheduler_type: reduce_lr_on_plateau mode: min factor: 0.1 patience: 10 min_lr: 0.00001 trainer: # HuggingFace Trainer-like arguments name: distill_attention token_reduce: true bottom_attention_only: false reverse_kl: false bf16: true train_split: train val_split: validation num_train_epochs: 2 gradient_accumulation_steps: 8 seed: 42 batch_size: 1 load_best_model_at_end: true greater_is_better: false metric_for_best_model: distill/eval/loss logging_steps: 100 evaluation_strategy: steps max_steps: -1 eval_steps: 100 max_eval_batches: null dataset: name: alpaca_clean dataset_config: name: alpaca path: yahma/alpaca-cleaned chunk_size: 1024 # 2048 concat_data: true cache_dir: '/u/scr/nlp/data/alpaca' pretrained_model_config: pretrained_model_name_or_path: 'mistralai/Mistral-7B-v0.1' cache_dir: '/juice/scr/scr110/scr/nlp/data/neo/hub/' preprocess_config: null dataloader: batch_size: 1 num_workers: 2 drop_last: false pin_memory: true optimizer: optim: adamw_torch_fused lr: 1e-4 weight_decay: 0.0 lr_scheduler: lr_scheduler_type: reduce_lr_on_plateau mode: min factor: 0.1 patience: 10 min_lr: 0.00001 trainer: # HuggingFace Trainer-like arguments name: default bf16: true train_split: train val_split: validation num_train_epochs: 2 gradient_accumulation_steps: 8 seed: 42 batch_size: 1 load_best_model_at_end: true greater_is_better: false metric_for_best_model: eval/loss # eval/rouge/geometric_mean logging_steps: 100 evaluation_strategy: steps max_steps: -1 eval_steps: 100 max_eval_batches: null finetune: method: lora kwargs: r: 8 lora_alpha: 16 # 32 lora_dropout: 0 # 0.05 target_modules: ['q_proj', 'k_proj', 'v_proj', 'o_proj'] ``` ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
davzoku/frankencria-llama2-11b-v1.3-m.1
davzoku
2024-02-14T13:29:27Z
739
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "llama-2", "en", "dataset:mlabonne/CodeLlama-2-20k", "arxiv:2312.15166", "base_model:davzoku/cria-llama2-7b-v1.3", "license:llama2", "autotrain_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-02-14T12:32:00Z
--- inference: false language: en license: llama2 model_type: llama datasets: - mlabonne/CodeLlama-2-20k pipeline_tag: text-generation base_model: - davzoku/cria-llama2-7b-v1.3 library_name: transformers tags: - mergekit - merge - llama-2 --- # FrankenCRIA v1.3-m.1 ## What is FrankenCRIA? <p align="center"> <img src="https://github.com/davzoku/cria/blob/main/assets/frankencria-icon-512x512.png?raw=true" width="300" height="300" alt="FrankenCRIA Logo"> <br> <i>This is a frankenmerge of <a href="https://huggingface.co/davzoku/cria-llama2-7b-v1.3">davzoku/cria-llama2-7b-v1.3</a>.</i> </p> The configuration is the same as [Undi95/Mistral-11B-v0.1](https://huggingface.co/Undi95/Mistral-11B-v0.1), [mlabonne/FrankenBeagle14-11B](https://huggingface.co/mlabonne/FrankenBeagle14-11B) and the DUS technique used in [upstage/SOLAR-10.7B-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-v1.0). Please be aware that this model is highly experimental, and no further training has been conducted following the merge. Therefore, the model performance may not meet expectations, as described in the [SOLAR paper](https://arxiv.org/abs/2312.15166) ## 📦 FrankenCRIA Model Release FrankenCRIA v1.3 comes with several variants. - [davzoku/frankencria-llama2-11b-v1.3-m.1](https://huggingface.co/davzoku/frankencria-llama2-11b-v1.3-m.1): 11B FrankenMerge inspired by [Undi95/Mistral-11B-v0.1](https://huggingface.co/Undi95/Mistral-11B-v0.1) - [davzoku/frankencria-llama2-11b-v1.3-m.2](https://huggingface.co/davzoku/frankencria-llama2-12.5b-v1.3-m.2): 12.5B interleaving FrankenMerge inspired by [vilm/vinallama-12.5b-chat-DUS](https://huggingface.co/vilm/vinallama-12.5b-chat-DUS) ## 🧩 Merge Details ### Merge Method This model was merged using the passthrough merge method. ### Models Merged The following models were included in the merge: * [davzoku/cria-llama2-7b-v1.3](https://huggingface.co/davzoku/cria-llama2-7b-v1.3) ### Configuration The following YAML configuration was used to produce this model. ```yaml # https://huggingface.co/Undi95/Mistral-11B-v0.1 slices: - sources: - model: davzoku/cria-llama2-7b-v1.3 layer_range: [0, 24] - sources: - model: davzoku/cria-llama2-7b-v1.3 layer_range: [8, 32] merge_method: passthrough dtype: bfloat16 ```
fzzhang/Marcoroni-neural-chat-7B-v2_gsm8k_merged_s
fzzhang
2024-02-16T20:54:51Z
739
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "en", "dataset:gsm8k", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-02-16T13:15:28Z
--- library_name: transformers license: apache-2.0 datasets: - gsm8k language: - en pipeline_tag: text-generation --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Kquant03/NeuralTrix-7B-dpo-relaser
Kquant03
2024-02-24T16:31:02Z
739
2
transformers
[ "transformers", "pytorch", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "mlabonne/OmniBeagle-7B", "flemmingmiguel/MBX-7B-v3", "AiMavenAi/AiMaven-Prometheus", "base_model:mlabonne/OmniBeagle-7B", "base_model:flemmingmiguel/MBX-7B-v3", "base_model:AiMavenAi/AiMaven-Prometheus", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-02-17T07:55:29Z
--- tags: - merge - mergekit - lazymergekit - mlabonne/OmniBeagle-7B - flemmingmiguel/MBX-7B-v3 - AiMavenAi/AiMaven-Prometheus base_model: - mlabonne/OmniBeagle-7B - flemmingmiguel/MBX-7B-v3 - AiMavenAi/AiMaven-Prometheus license: apache-2.0 --- # NeuralTrix-7B-v1 NeuralTrix-7B-v1 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [mlabonne/OmniBeagle-7B](https://huggingface.co/mlabonne/OmniBeagle-7B) * [flemmingmiguel/MBX-7B-v3](https://huggingface.co/flemmingmiguel/MBX-7B-v3) * [AiMavenAi/AiMaven-Prometheus](https://huggingface.co/AiMavenAi/AiMaven-Prometheus) It was then trained with DPO using: * https://huggingface.co/datasets/jondurbin/truthy-dpo-v0.1 ## 🧩 Configuration ```yaml models: - model: mistralai/Mistral-7B-v0.1 # no parameters necessary for base model - model: mlabonne/OmniBeagle-7B parameters: density: 0.65 weight: 0.4 - model: flemmingmiguel/MBX-7B-v3 parameters: density: 0.6 weight: 0.35 - model: AiMavenAi/AiMaven-Prometheus parameters: density: 0.6 weight: 0.35 merge_method: dare_ties base_model: mistralai/Mistral-7B-v0.1 parameters: int8_mask: true dtype: float16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "CultriX/NeuralTrix-7B-v1" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
Azure99/blossom-v4-qwen1_5-4b
Azure99
2024-02-20T02:42:07Z
739
0
transformers
[ "transformers", "safetensors", "qwen2", "text-generation", "zh", "en", "dataset:Azure99/blossom-chat-v2", "dataset:Azure99/blossom-math-v3", "dataset:Azure99/blossom-wizard-v2", "dataset:Azure99/blossom-orca-v2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-02-19T13:21:02Z
--- license: apache-2.0 datasets: - Azure99/blossom-chat-v2 - Azure99/blossom-math-v3 - Azure99/blossom-wizard-v2 - Azure99/blossom-orca-v2 language: - zh - en pipeline_tag: text-generation --- # **BLOSSOM-v4-qwen1_5-4b** [💻Github](https://github.com/Azure99/BlossomLM) • [🚀Blossom Chat Demo](https://blossom-chat.com/) ### 介绍 Blossom是一个对话式语言模型,基于Qwen1.5-4B预训练模型,在Blossom Orca/Wizard/Chat/Math混合数据集上进行指令精调得来。Blossom拥有强大的通用能力及上下文理解能力,此外,训练使用的高质量中英文数据集也进行了开源。 训练分为两阶段,第一阶段使用100K Wizard、100K Orca、20K Math单轮指令数据集,训练1个epoch;第二阶段使用50K Blossom chat多轮对话数据集、以及上一阶段中随机采样2%的数据,训练3个epoch。 ### 推理 推理采用对话续写的形式。 单轮对话 ``` A chat between a human and an artificial intelligence bot. The bot gives helpful, detailed, and polite answers to the human's questions. |Human|: 你好 |Bot|: ``` 多轮对话 ``` A chat between a human and an artificial intelligence bot. The bot gives helpful, detailed, and polite answers to the human's questions. |Human|: 你好 |Bot|: 你好,有什么我能帮助你的?<|endoftext|> |Human|: 介绍下中国的首都吧 |Bot|: ``` 注意:在历史对话的Bot输出结尾,拼接一个&lt;|endoftext|&gt;
yam-peleg/Experiment20-7B
yam-peleg
2024-02-20T03:22:38Z
739
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "chat", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-02-20T02:33:20Z
--- license: apache-2.0 language: - en library_name: transformers pipeline_tag: text-generation tags: - chat --- **Experiment20-7B** An experiment for testing and refining a specific training and evaluation pipeline research framework. This experiment aims to identify potential optimizations, focusing on data engineering, architecture efficiency, and evaluation performance. The goal is to evaluate the effectiveness of a new training / evaluation pipeline for LLMs. The experiment will explore adjustments in data preprocessing, model training algorithms, and evaluation metrics to test methods for improvement. More details in the future experiments. --- license: apache-2.0 ---
CultriX/MonaTrix-v4
CultriX
2024-02-20T23:13:20Z
739
1
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "Kukedlc/NeuralMaxime-7B-slerp", "eren23/ogno-monarch-jaskier-merge-7b", "eren23/dpo-binarized-NeutrixOmnibe-7B", "base_model:Kukedlc/NeuralMaxime-7B-slerp", "base_model:eren23/ogno-monarch-jaskier-merge-7b", "base_model:eren23/dpo-binarized-NeutrixOmnibe-7B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-02-20T11:45:19Z
--- tags: - merge - mergekit - lazymergekit - Kukedlc/NeuralMaxime-7B-slerp - eren23/ogno-monarch-jaskier-merge-7b - eren23/dpo-binarized-NeutrixOmnibe-7B base_model: - Kukedlc/NeuralMaxime-7B-slerp - eren23/ogno-monarch-jaskier-merge-7b - eren23/dpo-binarized-NeutrixOmnibe-7B license: apache-2.0 --- # MonaTrix-v4 MonaTrix-v4 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [Kukedlc/NeuralMaxime-7B-slerp](https://huggingface.co/Kukedlc/NeuralMaxime-7B-slerp) * [eren23/ogno-monarch-jaskier-merge-7b](https://huggingface.co/eren23/ogno-monarch-jaskier-merge-7b) * [eren23/dpo-binarized-NeutrixOmnibe-7B](https://huggingface.co/eren23/dpo-binarized-NeutrixOmnibe-7B) ## 🧩 Configuration ```yaml models: - model: mistralai/Mistral-7B-v0.1 # No parameters necessary for base model - model: Kukedlc/NeuralMaxime-7B-slerp #Emphasize the beginning of Vicuna format models parameters: weight: 0.36 density: 0.65 - model: eren23/ogno-monarch-jaskier-merge-7b parameters: weight: 0.34 density: 0.6 # Vicuna format - model: eren23/dpo-binarized-NeutrixOmnibe-7B parameters: weight: 0.3 density: 0.6 merge_method: dare_ties base_model: mistralai/Mistral-7B-v0.1 parameters: int8_mask: true dtype: bfloat16 random_seed: 0 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "CultriX/MonaTrix-v4" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
aloobun/Reyna-CoT-4B-v0.1
aloobun
2024-02-23T07:35:19Z
739
5
transformers
[ "transformers", "safetensors", "qwen2", "text-generation", "finetune", "synthetic data", "custom_code", "COT", "conversational", "dataset:kaist-ai/CoT-Collection", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-02-23T03:02:50Z
--- license_name: tongyi-qianwen-research license_link: https://huggingface.co/Qwen/Qwen1.5-1.8B-Chat/raw/main/LICENSE library_name: transformers license: other tags: - finetune - synthetic data - custom_code - qwen2 - COT datasets: - kaist-ai/CoT-Collection --- ![Reyna aloobun qwen4B](https://i.imgur.com/QfbOY6c.jpeg) - Finetuned [Qwen/Qwen1.5-4B](https://huggingface.co/Qwen/Qwen1.5-4B), on variety of CoT tasks including Reasoning, Closed Book Question Answering, Ethics, and more. - Datasets : Curated from - [kaist-ai/CoT-Collection](https://huggingface.co/datasets/kaist-ai/CoT-Collection), [euclaise/TinyCoT](https://huggingface.co/datasets/euclaise/TinyCoT) and a very small subset from [teknium/OpenHermes-2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5). - This marks the fourth model in this series. This experiment aims to improve Chain of Thought (CoT) capabilities on smaller language models. - I may rerun the finetuning experiment(with a more balanced dataset), using an iterative rationale-bootstrapping procedure inspired by euclaise/Memphis-CoT-3B. - Hyperparameter: adamw with eps of 1e-8, cosine decay with 20% warmup, lr=2e-5 ## Benchamrks: WIP ## Example: ``` from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer, StoppingCriteria import torch class MyStoppingCriteria(StoppingCriteria): def __init__(self, target_sequence, prompt): self.target_sequence = target_sequence self.prompt=prompt def __call__(self, input_ids, scores, **kwargs): generated_text = tokenizer.decode(input_ids[0]) generated_text = generated_text.replace(self.prompt,'') if self.target_sequence in generated_text: return True return False def __len__(self): return 1 def __iter__(self): yield self modelpath="aloobun/Reyna-CoT-4B-v0.1" model = AutoModelForCausalLM.from_pretrained( modelpath, torch_dtype=torch.bfloat16, device_map="cuda", trust_remote_code=True, ) tokenizer = AutoTokenizer.from_pretrained( modelpath, trust_remote_code=True, use_fast=False, ) prompt = "Avery opens a flower shop. She ties 8 bunches of flowers with 9 flowers in each bunch. How many bunches would she have if she put 12 flowers in each bunch instead?\n" encoded_input = tokenizer(prompt, return_tensors='pt') input_ids=encoded_input['input_ids'].cuda() streamer = TextStreamer(tokenizer=tokenizer, skip_prompt=True) op = model.generate( input_ids, streamer=streamer, pad_token_id=tokenizer.eos_token_id, do_sample=True, temperature=0.6, top_p=0.8, max_new_tokens=512, stopping_criteria=MyStoppingCriteria("<|endoftext|>", prompt) ) ``` ## Output: >She would have 8 x 9 = 72 flowers in total. >She would have 72 / 12 = 6 bunches of flowers with 12 flowers in each bunch. >Therefore, the answer is 6.<|endoftext|>
vishnukv/speechless-mistral-dolphin-orca-platypus-samantha-WestSeverusJaskier-7b
vishnukv
2024-03-05T10:11:11Z
739
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "base_model:vishnukv/WestSeverusJaskier", "base_model:uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-03-05T10:03:48Z
--- base_model: - vishnukv/WestSeverusJaskier - uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b library_name: transformers tags: - mergekit - merge license: mit --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [vishnukv/WestSeverusJaskier](https://huggingface.co/vishnukv/WestSeverusJaskier) * [uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b](https://huggingface.co/uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b)
Eric111/MistInst-v0.2_ochat-3.5-0106_dpo-binarized-NeuralTrix-7B
Eric111
2024-03-06T21:56:59Z
739
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "Eric111/Mistral-7B-Instruct-v0.2_openchat-3.5-0106", "eren23/dpo-binarized-NeuralTrix-7B", "conversational", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-03-06T21:53:07Z
--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - Eric111/Mistral-7B-Instruct-v0.2_openchat-3.5-0106 - eren23/dpo-binarized-NeuralTrix-7B --- # MistInst-v0.2_ochat-3.5-0106_dpo-binarized-NeuralTrix-7B MistInst-v0.2_ochat-3.5-0106_dpo-binarized-NeuralTrix-7B is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [Eric111/Mistral-7B-Instruct-v0.2_openchat-3.5-0106](https://huggingface.co/Eric111/Mistral-7B-Instruct-v0.2_openchat-3.5-0106) * [eren23/dpo-binarized-NeuralTrix-7B](https://huggingface.co/eren23/dpo-binarized-NeuralTrix-7B) ## 🧩 Configuration ```yaml slices: - sources: - model: Eric111/Mistral-7B-Instruct-v0.2_openchat-3.5-0106 layer_range: [0, 32] - model: eren23/dpo-binarized-NeuralTrix-7B layer_range: [0, 32] merge_method: slerp base_model: Eric111/Mistral-7B-Instruct-v0.2_openchat-3.5-0106 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```
ChaoticNeutrals/Eris_Remix_DPO_7B
ChaoticNeutrals
2024-03-07T02:14:35Z
739
2
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "en", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-03-07T01:22:05Z
--- base_model: [] library_name: transformers license: other language: - en --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/642265bc01c62c1e4102dc36/Jcg-4l6zVlPHVKOoxjmkG.png) # Jeitral: "Eris, the Greek goddess of chaos and discord." Notes: Model should be excellent for both RP/Chat related tasks. Seems to be working in both Alpaca/Chatml. Collaborative effort from both @Jeiku and @Nitral involving what we currently felt were our best individual projects. We hope you enjoy! - The Chaotic Neutrals. # Remix with DPO: https://huggingface.co/datasets/athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW Trained for 200 steps/ 1 epoch Base model used: https://huggingface.co/ChaoticNeutrals/Eris_Remix_7B
Gille/StrangeMerges_33-7B-slerp
Gille
2024-03-07T12:43:42Z
739
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "ZySec-AI/ZySec-7B-v1", "Gille/StrangeMerges_30-7B-slerp", "conversational", "base_model:ZySec-AI/ZySec-7B-v1", "base_model:Gille/StrangeMerges_30-7B-slerp", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-03-07T11:58:05Z
--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - ZySec-AI/ZySec-7B-v1 - Gille/StrangeMerges_30-7B-slerp base_model: - ZySec-AI/ZySec-7B-v1 - Gille/StrangeMerges_30-7B-slerp --- # StrangeMerges_33-7B-slerp StrangeMerges_33-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [ZySec-AI/ZySec-7B-v1](https://huggingface.co/ZySec-AI/ZySec-7B-v1) * [Gille/StrangeMerges_30-7B-slerp](https://huggingface.co/Gille/StrangeMerges_30-7B-slerp) ## 🧩 Configuration ```yaml slices: - sources: - model: ZySec-AI/ZySec-7B-v1 layer_range: [0, 32] - model: Gille/StrangeMerges_30-7B-slerp layer_range: [0, 32] merge_method: slerp base_model: ZySec-AI/ZySec-7B-v1 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 0.3, 0.5, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0.7, 0.5, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Gille/StrangeMerges_33-7B-slerp" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
Locutusque/ChatHercules-2.5-Mistral-7B-DPO
Locutusque
2024-03-10T04:38:10Z
739
4
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "merge", "en", "dataset:argilla/distilabel-intel-orca-dpo-pairs", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-03-10T00:43:13Z
--- library_name: transformers tags: - merge license: apache-2.0 datasets: - argilla/distilabel-intel-orca-dpo-pairs language: - en --- # ChatHercules-2.5-Mistral-7B-DPO ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6437292ecd93f4c9a34b0d47/VW32vrPx2giqo5Od8Tyz0.png) ChatHercules-2.5-Mistral-7B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [Locutusque/Hercules-2.5-Mistral-7B](https://huggingface.co/Locutusque/Hercules-2.5-Mistral-7B) * [openchat/openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106) I then use DPO to fine-tune the product of the merge on 20% of argilla/distilabel-intel-orca-dpo-pairs ## 🧩 Configuration ```yaml slices: - sources: - model: Locutusque/Hercules-2.5-Mistral-7B layer_range: [0, 32] - model: openchat/openchat-3.5-0106 layer_range: [0, 32] merge_method: slerp base_model: Locutusque/Hercules-2.5-Mistral-7B parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "hydra-project/ChatHercules-2.5-Mistral-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ``` ## Evaluation results ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6437292ecd93f4c9a34b0d47/Rua1QoEPYNPqL1Z1W4dpf.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6437292ecd93f4c9a34b0d47/44UUHS9xx5gtCUhvLVdAo.png)
automerger/Experiment28Yam-7B
automerger
2024-03-10T17:57:31Z
739
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "automerger", "base_model:mayacinka/yam-jom-7B-slerp", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-03-10T13:18:49Z
--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - automerger base_model: - mayacinka/yam-jom-7B-slerp --- # Experiment28Yam-7B Experiment28Yam-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration. * [mayacinka/yam-jom-7B-slerp](https://huggingface.co/mayacinka/yam-jom-7B-slerp) ## 🧩 Configuration ```yaml models: - model: yam-peleg/Experiment28-7B # No parameters necessary for base model - model: mayacinka/yam-jom-7B-slerp parameters: density: 0.53 weight: 0.6 merge_method: dare_ties base_model: yam-peleg/Experiment28-7B parameters: int8_mask: true dtype: bfloat16 random_seed: 0 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "automerger/Experiment28Yam-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
Abhaykoul/HelpingAI-Lite-4x1b
Abhaykoul
2024-03-19T06:30:10Z
739
0
transformers
[ "transformers", "safetensors", "mixtral", "text-generation", "moe", "mergekit", "vortexmergekit", "OEvortex/HelpingAI-unvelite", "OEvortex/lite-hermes", "OEvortex/HelpingAI-Lite", "OEvortex/HelpingAI-Lite-1.5T", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-03-10T14:47:47Z
--- license: apache-2.0 inference: false tags: - moe - mergekit - vortexmergekit - OEvortex/HelpingAI-unvelite - OEvortex/lite-hermes - OEvortex/HelpingAI-Lite - OEvortex/HelpingAI-Lite-1.5T --- # HelpingAI-Lite-4x1b Hey there! 👋 Welcome to the HelpingAI-Lite-4x1b! This is a MOE brought together using the awesome [VortexMerge kit](https://colab.research.google.com/drive/1YjcvCLuNG1PK7Le6_4xhVU5VpzTwvGhk#scrollTo=UG5H2TK4gVyl). Let's see what we've got in this model: * [OEvortex/HelpingAI-unvelite](https://huggingface.co/OEvortex/HelpingAI-unvelite) 🚀 * [OEvortex/lite-hermes](https://huggingface.co/OEvortex/lite-hermes) 🚀 * [OEvortex/HelpingAI-Lite](https://huggingface.co/OEvortex/HelpingAI-Lite) 🚀 * [OEvortex/HelpingAI-Lite-1.5T](https://huggingface.co/OEvortex/HelpingAI-Lite-1.5T) 🚀 ## 🧩 Configuration ```yaml base_model: OEvortex/HelpingAI-Lite dtype: float16 gate_mode: cheap_embed experts: - source_model: OEvortex/HelpingAI-unvelite positive_prompts: ["You are an helpful unbounded, uncensored assistant."] - source_model: OEvortex/lite-hermes positive_prompts: ["You are helpful assistant."] - source_model: OEvortex/HelpingAI-Lite positive_prompts: ["You are a coding assistant."] - source_model: OEvortex/HelpingAI-Lite-1.5T positive_prompts: ["You are helpful and general-purpose assistant."]
eren23/Experiment26-12B
eren23
2024-03-13T08:39:22Z
739
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "yam-peleg/Experiment26-7B", "en", "base_model:yam-peleg/Experiment26-7B", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-03-13T08:29:12Z
--- tags: - merge - mergekit - lazymergekit - yam-peleg/Experiment26-7B - yam-peleg/Experiment26-7B - yam-peleg/Experiment26-7B - yam-peleg/Experiment26-7B - yam-peleg/Experiment26-7B - yam-peleg/Experiment26-7B - yam-peleg/Experiment26-7B base_model: - yam-peleg/Experiment26-7B - yam-peleg/Experiment26-7B - yam-peleg/Experiment26-7B - yam-peleg/Experiment26-7B - yam-peleg/Experiment26-7B - yam-peleg/Experiment26-7B - yam-peleg/Experiment26-7B license: cc-by-nc-4.0 language: - en --- # Experiment26-12B Experiment26-12B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [yam-peleg/Experiment26-7B](https://huggingface.co/yam-peleg/Experiment26-7B) * [yam-peleg/Experiment26-7B](https://huggingface.co/yam-peleg/Experiment26-7B) * [yam-peleg/Experiment26-7B](https://huggingface.co/yam-peleg/Experiment26-7B) * [yam-peleg/Experiment26-7B](https://huggingface.co/yam-peleg/Experiment26-7B) * [yam-peleg/Experiment26-7B](https://huggingface.co/yam-peleg/Experiment26-7B) * [yam-peleg/Experiment26-7B](https://huggingface.co/yam-peleg/Experiment26-7B) * [yam-peleg/Experiment26-7B](https://huggingface.co/yam-peleg/Experiment26-7B) ## 🧩 Configuration ```yaml dtype: float16 merge_method: passthrough slices: - sources: - layer_range: [0, 8] model: yam-peleg/Experiment26-7B - sources: - layer_range: [4, 12] model: yam-peleg/Experiment26-7B - sources: - layer_range: [8, 16] model: yam-peleg/Experiment26-7B - sources: - layer_range: [12, 20] model: yam-peleg/Experiment26-7B - sources: - layer_range: [16, 24] model: yam-peleg/Experiment26-7B - sources: - layer_range: [20, 28] model: yam-peleg/Experiment26-7B - sources: - layer_range: [24, 32] model: yam-peleg/Experiment26-7B ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "eren23/Experiment26-12B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
yam-peleg/gemma-7b-experiment
yam-peleg
2024-03-16T14:21:18Z
739
0
transformers
[ "transformers", "safetensors", "gemma", "text-generation", "chat", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-03-16T13:42:33Z
--- license: apache-2.0 language: - en library_name: transformers pipeline_tag: text-generation tags: - chat --- **gemma-7b-experiment** **This is just an experiment placeholder for testing out local validation strategy, there is absolutly no real reason for you to try this model. it has nothing new into it.** An experiment for testing and refining a local cross validation strategy. The goal is to evaluate LLMs locally and make sure the scores obtained locally can be reproduced publiclly. More details coming soon. --- license: apache-2.0 ---
Chickaboo/ChickaQ-V2-Large-Beta
Chickaboo
2024-03-17T16:55:45Z
739
1
transformers
[ "transformers", "safetensors", "qwen2", "text-generation", "mergekit", "merge", "conversational", "base_model:vilm/Quyen-mini-v0.1", "base_model:Qwen/Qwen1.5-1.8B-Chat", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-03-17T05:18:50Z
--- base_model: - vilm/Quyen-mini-v0.1 - Qwen/Qwen1.5-1.8B-Chat library_name: transformers tags: - mergekit - merge license: mit --- # Models in the ChickaQ family - **ChickaQ (0.5B)** - **ChickaQ-Large (1.8B)** - **ChickaQ-V2-Beta (0.9B)** - **ChickaQ-V2-Large-Beta (3B)** # mergedmodel This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the passthrough merge method. ### Models Merged The following models were included in the merge: * [vilm/Quyen-mini-v0.1](https://huggingface.co/vilm/Quyen-mini-v0.1) * [Qwen/Qwen1.5-1.8B-Chat](https://huggingface.co/Qwen/Qwen1.5-1.8B-Chat) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: Qwen/Qwen1.5-1.8B-Chat layer_range: [0, 24] - sources: - model: vilm/Quyen-mini-v0.1 layer_range: [0, 24] merge_method: passthrough dtype: bfloat16 ``` ### License --- license: mit license_link: [mit](https://mit-license.org/) ---
mvpmaster/NeuralDareDMistralPro-7b-slerp
mvpmaster
2024-03-19T23:59:52Z
739
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "mlabonne/NeuralDaredevil-7B", "NousResearch/Hermes-2-Pro-Mistral-7B", "base_model:mlabonne/NeuralDaredevil-7B", "base_model:NousResearch/Hermes-2-Pro-Mistral-7B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-03-18T23:52:49Z
--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - mlabonne/NeuralDaredevil-7B - NousResearch/Hermes-2-Pro-Mistral-7B base_model: - mlabonne/NeuralDaredevil-7B - NousResearch/Hermes-2-Pro-Mistral-7B --- # NeuralDareDMistralPro-slerp NeuralDareDMistralPro-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [mlabonne/NeuralDaredevil-7B](https://huggingface.co/mlabonne/NeuralDaredevil-7B) * [NousResearch/Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B) ## 🧩 Configuration ```yaml slices: - sources: - model: mlabonne/NeuralDaredevil-7B layer_range: [0, 32] - model: NousResearch/Hermes-2-Pro-Mistral-7B layer_range: [0, 32] merge_method: slerp base_model: mlabonne/NeuralDaredevil-7B parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mvpmaster/NeuralDareDMistralPro-slerp" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
ontocord/Felix-8B
ontocord
2024-03-26T03:48:14Z
739
25
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-03-23T10:54:30Z
--- license: apache-2.0 language: - en --- March 23, 2024 # Felix-8B: A model built with lawfulness alignment Felix-8B is an experimental language model developed by Ontocord.ai, specializing in addressing lawfulness concerns under the Biden-Harris Executive Order on AI and the principles of the EU AI Act. This model has achieved one of the highest scores on the TruthfulQA benchmark compared to models of its size, showcasing its exceptional performance in providing accurate and reliable responses. Felix-8B is **experimental and a research work product** and a DPO reinforcement learning version of [ontocord/sft-4e-exp2](https://huggingface.co/ontocord/sft-4e-exp2) which in turn is a fine-tuned version of [TencentARC/Mistral_Pro_8B_v0.1](https://huggingface.co/TencentARC/Mistral_Pro_8B_v0.1). **If you find any issues please let us know in the Community section so we can improve the model.** ![image/png](https://cdn-uploads.huggingface.co/production/uploads/630430583926de1f7ec62c6b/zKoEI-B21KnmeRWvKzrx1.png) ## Model Description Felix-8B is an 8 billion parameter language model trained using Ontocord.ai's proprietary auto-purpleteaming technique. The model has been fine-tuned and optimized using synthetic data, with the goal of improving its robustness and ability to handle a wide range of tasks while maintaining a strong focus on safety and truthfulness. ## Evaluation | arc (25) | hellaswag (10) | mmlu (5) | truthfulqa_mc (20) | winogrande | gsm8k (exact) | avg (exact) | |:--------:|:--------------:|:--------:|:------------------:|:----------:|:-------------:|:-----------:| | 0.6519 | 0.8474 | 0.6057 | 0.638 | 0.764 | 0.5997 | 0.68445 | Felix-8B has been extensively evaluated on various benchmarks, including the TruthfulQA benchmark, where it has achieved one of the highest scores among models of its size. We also confirmed that the model increased its helpfulness during DPO, which is very encouraging as we did not specifically target helpfulness, but rather harmlessness. More evaluations will be provided as we further continue our research. ## Redteaming Considerations Our redteam focuses on specific areas of concern under the executive order, including: * Harm to oneself or others. * Requests on how to create cyber-attacks. * Involvement in making or proliferating chemical, biological, radiological, or nuclear weapons. * Participation in any illegal act. * Infringement of privacy or rights. * Attempts to circumvent redteam controls. Our redteam focuses on the potential coverage of the EU AI Act, including: * Misinformation * Social Scoring * Emotion Monitoring and Control in Schools and Workplaces * Predictive Policing * Classification of People Based on Sensitive Characteristics We also address traditional concerns related to AI Safety: * Toxic language * Fairness * Stereotyping and Discrimination * Adversarial Prompting * Out of Distribution Robustness * Machine Ethics * Avoidance of unqualified professional advice. ## Dataset The SFT model [ontocord/sft-4e-exp2](https://huggingface.co/ontocord/sft-4e-exp2) was trained with a subset of the execellent [OpenHermes 2.5 dataset](https://huggingface.co/datasets/teknium/OpenHermes-2.5). The DPO reinforcement learning uses an Ontocord synthetic auto-purpletteaming dataset. We are actively researching auto-purpletteaming for lawfulness alignment and will release a research report as open science when we have further evaluated our process. An example of our dataset entry is as follows: ``` <|im_start|>user Explain it simply for someone who doesn't understand much about the topic. Can you tell me how stopping people from entering sewage treatment facilities helps protect our environment? <|im_end|> <|im_start|>assistant Sure! Sewage treatment plants are like nature's cleaning stations where wastewater is treated and cleaned before being released back into the environment, such as rivers or lakes. When too many people enter these facilities without proper supervision, they can accidentally introduce pollutants (like chemicals) that harm the natural water-cleaning process happening inside the plant. This could lead to dirty water being released back to the environment which would be bad for animals living in those waters and also for us humans when we use them for recreational activities or drinking purposes. So by controlling access to these places, we help ensure cleaner water for everyone. ``` ## Limitations and Biases While Felix-8B has been designed with safety and truthfulness as its primary focus, it is essential to acknowledge that no language model is entirely free from biases or limitations. Users should be aware that the model's outputs may be influenced by the data it was trained on and the inherent biases present in the training data. Additionally, Felix-8B's performance may vary depending on the specific task and domain. Users should exercise caution when applying the model to sensitive or high-stakes situations and always verify the model's outputs with human oversight. Felix-8B is **experimental and a research work product**. We have found for example, the model may be overly verbose which may be due to our SFT or the TencentARC/Mistral_Pro_8B_v0.1 model. Also, we are actively working on improving the training dataset. We have for example discovered some answers are too short in our dataset, or may have typos due to our use of a quantized generative model to create synthetic triples for DPO. ## Ethical Considerations Ontocord.ai is committed to developing AI technologies that prioritize lawfulness and equal access. Felix-8B has been designed with these principles in mind, and efforts have been made to mitigate potential risks and biases during the training process. However, users should be aware of the potential misuse of language models and ensure that Felix-8B is used in compliance with ethical guidelines and applicable laws and regulations. ## How to Use To use Felix-8B, you can access the model through the Hugging Face Model Hub: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ontocord/Felix-8B") model = AutoModelForCausalLM.from_pretrained("ontocord/Felix-8B") ``` Instruction format: ``` <|im_start|>system You are a helpful AI assistant.<|im_end|> <|im_start|>user Hello, who are you?<|im_end|> <|im_start|>assistant ``` ## Acknowledgement We want to thank [Teknium](https://huggingface.co/teknium) and [Nous Research](https://huggingface.co/NousResearch) for the excellent work in performant models. We would also like to thank our friends from [DFKI](https://www.dfki.de/en/web/research) and our friends Simone Tedeschi and Felix Friedrich for discussions on auto-purpleteaming. ## Citation To cite Felix-8B, please use: ``` @misc{ontocord2024Felix-8B, title={Felix-8B: A model built with lawfulness alignment }, author={Quan Nguyen, Vincent Hines, Huu Nguyen, Patrick Schramowski}, year={2024}, } ``` ** We will update the contributor list as we further develop this research.
Eric111/UltraCatunaMayo
Eric111
2024-03-23T14:15:03Z
739
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "mlabonne/UltraMerge-7B", "Eric111/CatunaMayo", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-03-23T14:11:46Z
--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - mlabonne/UltraMerge-7B - Eric111/CatunaMayo --- # UltraCatunaMayo UltraCatunaMayo is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [mlabonne/UltraMerge-7B](https://huggingface.co/mlabonne/UltraMerge-7B) * [Eric111/CatunaMayo](https://huggingface.co/Eric111/CatunaMayo) ## 🧩 Configuration ```yaml slices: - sources: - model: mlabonne/UltraMerge-7B layer_range: [0, 32] - model: Eric111/CatunaMayo layer_range: [0, 32] merge_method: slerp base_model: mlabonne/UltraMerge-7B parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```
Josephgflowers/Tinyllama-320M-Cinder-v1
Josephgflowers
2024-03-26T02:45:13Z
739
1
transformers
[ "transformers", "safetensors", "llama", "text-generation", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-03-24T20:34:40Z
--- license: mit widget: - text: '<|user|> Can you tell me a space adventure story?</s> <|assistant|>' --- Overview Cinder is an AI chatbot tailored for engaging users in scientific and educational conversations, offering companionship, and sparking imaginative exploration. This model was built by using a 220M tinyllama and depth upscalling and retraining with the Cinder dataset.
nbeerbower/bruphin-kappa
nbeerbower
2024-03-24T23:44:34Z
739
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "base_model:nbeerbower/bruphin-iota", "base_model:nbeerbower/bruphin-epsilon", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-03-24T23:41:15Z
--- license: apache-2.0 base_model: - nbeerbower/bruphin-iota - nbeerbower/bruphin-epsilon library_name: transformers tags: - mergekit - merge --- # bruphin-kappa This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [nbeerbower/bruphin-iota](https://huggingface.co/nbeerbower/bruphin-iota) * [nbeerbower/bruphin-epsilon](https://huggingface.co/nbeerbower/bruphin-epsilon) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: nbeerbower/bruphin-iota layer_range: [0, 32] - model: nbeerbower/bruphin-epsilon layer_range: [0, 32] merge_method: slerp base_model: nbeerbower/bruphin-epsilon parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```
mradermacher/iris-7b-GGUF
mradermacher
2024-05-06T05:52:26Z
739
0
transformers
[ "transformers", "gguf", "finetuned", "en", "ko", "base_model:davidkim205/iris-7b", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
2024-03-27T19:16:18Z
--- base_model: davidkim205/iris-7b language: - en - ko library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - finetuned --- ## About static quants of https://huggingface.co/davidkim205/iris-7b <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/iris-7b-GGUF/resolve/main/iris-7b.Q2_K.gguf) | Q2_K | 3.0 | | | [GGUF](https://huggingface.co/mradermacher/iris-7b-GGUF/resolve/main/iris-7b.IQ3_XS.gguf) | IQ3_XS | 3.3 | | | [GGUF](https://huggingface.co/mradermacher/iris-7b-GGUF/resolve/main/iris-7b.Q3_K_S.gguf) | Q3_K_S | 3.4 | | | [GGUF](https://huggingface.co/mradermacher/iris-7b-GGUF/resolve/main/iris-7b.IQ3_S.gguf) | IQ3_S | 3.4 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/iris-7b-GGUF/resolve/main/iris-7b.IQ3_M.gguf) | IQ3_M | 3.5 | | | [GGUF](https://huggingface.co/mradermacher/iris-7b-GGUF/resolve/main/iris-7b.Q3_K_M.gguf) | Q3_K_M | 3.8 | lower quality | | [GGUF](https://huggingface.co/mradermacher/iris-7b-GGUF/resolve/main/iris-7b.Q3_K_L.gguf) | Q3_K_L | 4.1 | | | [GGUF](https://huggingface.co/mradermacher/iris-7b-GGUF/resolve/main/iris-7b.IQ4_XS.gguf) | IQ4_XS | 4.2 | | | [GGUF](https://huggingface.co/mradermacher/iris-7b-GGUF/resolve/main/iris-7b.Q4_0.gguf) | Q4_0 | 4.4 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/iris-7b-GGUF/resolve/main/iris-7b.Q4_K_S.gguf) | Q4_K_S | 4.4 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/iris-7b-GGUF/resolve/main/iris-7b.IQ4_NL.gguf) | IQ4_NL | 4.4 | prefer IQ4_XS | | [GGUF](https://huggingface.co/mradermacher/iris-7b-GGUF/resolve/main/iris-7b.Q4_K_M.gguf) | Q4_K_M | 4.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/iris-7b-GGUF/resolve/main/iris-7b.Q5_K_S.gguf) | Q5_K_S | 5.3 | | | [GGUF](https://huggingface.co/mradermacher/iris-7b-GGUF/resolve/main/iris-7b.Q5_K_M.gguf) | Q5_K_M | 5.4 | | | [GGUF](https://huggingface.co/mradermacher/iris-7b-GGUF/resolve/main/iris-7b.Q6_K.gguf) | Q6_K | 6.2 | very good quality | | [GGUF](https://huggingface.co/mradermacher/iris-7b-GGUF/resolve/main/iris-7b.Q8_0.gguf) | Q8_0 | 7.9 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
LeroyDyer/Mixtral_AI_Cyber_3.0
LeroyDyer
2024-04-09T16:24:51Z
739
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "code", "Cyber-Series", "conversational", "en", "arxiv:2306.01708", "base_model:LeroyDyer/Mixtral_AI_Cyber_2.0", "base_model:LeroyDyer/Mixtral_AI_Cyber_1.0", "base_model:LeroyDyer/Mixtral_Chat_X_128k", "base_model:LeroyDyer/Mixtral_instruct", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-03-29T09:40:56Z
--- base_model: - LeroyDyer/Mixtral_AI_Cyber_2.0 - LeroyDyer/Mixtral_AI_Cyber_1.0 - LeroyDyer/Mixtral_Chat_X_128k - LeroyDyer/Mixtral_instruct library_name: transformers tags: - mergekit - merge - code - Cyber-Series license: mit language: - en --- A Merge of the top models from this repo! with models like this .... why third party ! # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [LeroyDyer/Mixtral_AI_Cyber_2.0](https://huggingface.co/LeroyDyer/Mixtral_AI_Cyber_2.0) as a base. ### Models Merged The following models were included in the merge: * [LeroyDyer/Mixtral_AI_Cyber_1.0](https://huggingface.co/LeroyDyer/Mixtral_AI_Cyber_1.0) * [LeroyDyer/Mixtral_Chat_X_128k](https://huggingface.co/LeroyDyer/Mixtral_Chat_X_128k) * [LeroyDyer/Mixtral_instruct](https://huggingface.co/LeroyDyer/Mixtral_instruct) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: LeroyDyer/Mixtral_AI_Cyber_1.0 parameters: density: [1, 0.721, 0.1] # density gradient weight: 1.0 - model: LeroyDyer/Mixtral_instruct parameters: density: 0.5 weight: [0, 0.3, 0.437, 1] # weight gradient - model: LeroyDyer/Mixtral_Chat_X_128k parameters: density: 0.33 weight: - filter: mlp value: 0.5 - value: 0 merge_method: ties base_model: LeroyDyer/Mixtral_AI_Cyber_2.0 parameters: normalize: true int8_mask: true dtype: float16 ```
Kukedlc/Neural-4-QA-7b
Kukedlc
2024-04-06T03:04:07Z
739
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "yam-peleg/Experiment21-7B", "CultriX/NeuralTrix-bf16", "louisgrc/Montebello_7B_SLERP", "CorticalStack/pastiche-crown-clown-7b-dare-dpo", "chihoonlee10/T3Q-Mistral-Orca-Math-DPO", "base_model:yam-peleg/Experiment21-7B", "base_model:CultriX/NeuralTrix-bf16", "base_model:louisgrc/Montebello_7B_SLERP", "base_model:CorticalStack/pastiche-crown-clown-7b-dare-dpo", "base_model:chihoonlee10/T3Q-Mistral-Orca-Math-DPO", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-03-30T03:46:36Z
--- tags: - merge - mergekit - lazymergekit - yam-peleg/Experiment21-7B - CultriX/NeuralTrix-bf16 - louisgrc/Montebello_7B_SLERP - CorticalStack/pastiche-crown-clown-7b-dare-dpo - chihoonlee10/T3Q-Mistral-Orca-Math-DPO base_model: - yam-peleg/Experiment21-7B - CultriX/NeuralTrix-bf16 - louisgrc/Montebello_7B_SLERP - CorticalStack/pastiche-crown-clown-7b-dare-dpo - chihoonlee10/T3Q-Mistral-Orca-Math-DPO license: apache-2.0 --- # Neural-4-QA-7b Neural-4-QA-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [yam-peleg/Experiment21-7B](https://huggingface.co/yam-peleg/Experiment21-7B) * [CultriX/NeuralTrix-bf16](https://huggingface.co/CultriX/NeuralTrix-bf16) * [louisgrc/Montebello_7B_SLERP](https://huggingface.co/louisgrc/Montebello_7B_SLERP) * [CorticalStack/pastiche-crown-clown-7b-dare-dpo](https://huggingface.co/CorticalStack/pastiche-crown-clown-7b-dare-dpo) * [chihoonlee10/T3Q-Mistral-Orca-Math-DPO](https://huggingface.co/chihoonlee10/T3Q-Mistral-Orca-Math-DPO) ## 🧩 Configuration ```yaml models: - model: chihoonlee10/T3Q-Mistral-Orca-Math-DPO # No parameters necessary for base model - model: yam-peleg/Experiment21-7B parameters: density: 0.66 weight: 0.2 - model: CultriX/NeuralTrix-bf16 parameters: density: 0.55 weight: 0.2 - model: louisgrc/Montebello_7B_SLERP parameters: density: 0.55 weight: 0.2 - model: CorticalStack/pastiche-crown-clown-7b-dare-dpo parameters: density: 0.44 weight: 0.2 - model: chihoonlee10/T3Q-Mistral-Orca-Math-DPO parameters: density: 0.66 weight: 0.2 merge_method: dare_ties base_model: chihoonlee10/T3Q-Mistral-Orca-Math-DPO parameters: int8_mask: true dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Kukedlc/Neural-4-QA-7b" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
jisukim8873/mistral-7B-alpaca-case-0-2
jisukim8873
2024-04-01T04:27:05Z
739
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-04-01T02:38:18Z
--- library_name: transformers license: apache-2.0 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
chrischain/SatoshiNv5
chrischain
2024-04-02T03:19:23Z
739
2
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "finance", "legal", "biology", "art", "en", "license:cc-by-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-04-01T21:04:58Z
--- license: cc-by-2.0 language: - en tags: - finance - legal - biology - art --- Behold, one of the first fine-tunes of Mistral's 7B 0.2 Base model. SatoshiN is trained on 4 epochs 2e-4 learning rate (cosine) of a diverse custom data-set, combined with a polishing round of that same data-set at a 1e-4 linear learning rate. It's a nice assistant that isn't afraid to ask questions, and gather additional information before providing a response to user prompts. SatoshiN | Base-Model Wikitext Perplexity: 6.27 | 5.4 **Similar to SOTA, this model runs a bit hot, try using lower temperatures below .5 if experiencing any nonsense)
Locutusque/Hercules-4.0-Yi-34B
Locutusque
2024-04-07T15:27:48Z
739
3
transformers
[ "transformers", "safetensors", "llama", "text-generation", "medical", "science", "biology", "chemistry", "not-for-all-audiences", "conversational", "en", "dataset:Locutusque/hercules-v4.0", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-04-02T10:39:11Z
--- library_name: transformers tags: - medical - science - biology - chemistry - not-for-all-audiences license: apache-2.0 datasets: - Locutusque/hercules-v4.0 language: - en --- # Hercules-4.0-Yi-34B ![image](https://th.bing.com/th/id/OIG4.If5016ryQjn_jMAmKd5X?dpr=2.6&pid=ImgDetMain) ## Model Description Hercules-4.0-Yi-34B is a fine-tuned language model derived from Yi-34B language model. It is specifically designed to excel in instruction following, function calls, and conversational interactions across various scientific and technical domains. The dataset used for fine-tuning, also named Hercules-v4.0, expands upon the diverse capabilities of OpenHermes-2.5 with contributions from numerous curated datasets. This fine-tuning has hercules-v4.0 with enhanced abilities in: - Complex Instruction Following: Understanding and accurately executing multi-step instructions, even those involving specialized terminology. - Function Calling: Seamlessly interpreting and executing function calls, providing appropriate input and output values. - Domain-Specific Knowledge: Engaging in informative and educational conversations about Biology, Chemistry, Physics, Mathematics, Medicine, Computer Science, and more. ## Intended Uses & Potential Bias Hercules-4.0-Yi-34B is well-suited to the following applications: - Specialized Chatbots: Creating knowledgeable chatbots and conversational agents in scientific and technical fields. - Instructional Assistants: Supporting users with educational and step-by-step guidance in various disciplines. - Code Generation and Execution: Facilitating code execution through function calls, aiding in software development and prototyping. **Important Note: Although Hercules-v4.0 is carefully constructed, it's important to be aware that the underlying data sources may contain biases or reflect harmful stereotypes. Use this model with caution and consider additional measures to mitigate potential biases in its responses.** # Quants GGUF: https://huggingface.co/bartowski/Hercules-4.0-Yi-34B-GGUF ExLlamaV2: https://huggingface.co/bartowski/Hercules-4.0-Yi-34B-exl2 ## Limitations and Risks - Toxicity: The dataset contains toxic or harmful examples. - Hallucinations and Factual Errors: Like other language models, Hercules-4.0-Yi-34B may generate incorrect or misleading information, especially in specialized domains where it lacks sufficient expertise. - Potential for Misuse: The ability to engage in technical conversations and execute function calls could be misused for malicious purposes. ## Training Procedure - This model was trained on 8 kaggle TPUs, using torch xla SPMD for high MXU efficiency. There was no expense on my end (meaning you can reproduce this too!) - a learning rate of 1e-4 with an end factor of 0.001 was used. - No mixed precision was used, with the default dtype being bfloat16. - A total batch size of 64 was used. - Trained on 75,000 examples of Hercules-v4.0 - I used LoRA to freeze about 97% of the model parameters. Quantization was not used. - This model was trained on OpenAI's ChatML prompt format. Because this model has function calling capabilities, the prompt format is slightly different, here's what it would look like: ```<|im_start|>system\n{message}<|im_end|>\n<|im_start|>user\n{user message}<|im_end|>\n<|im_start|>call\n{function call message}<|im_end|>\n<|im_start|>function\n{function response message}<|im_end|>\n<|im_start|>assistant\n{assistant message}<|endoftext|>``` This model was fine-tuned using my TPU-Alignment repository. https://github.com/Locutusque/TPU-Alignment ## Evaluation Coming Soon
MiniMoog/Mergerix-7b-v0.2
MiniMoog
2024-04-02T20:35:56Z
739
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-04-02T20:20:23Z
--- tags: - merge - mergekit - lazymergekit license: apache-2.0 --- # Mergerix-7b-v0.2 Mergerix-7b-v0.2 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): ## 🧩 Configuration ```yaml models: - model: alpindale/Mistral-7B-v0.2-hf - model: liminerity/M7-7b - model: rwitz/experiment26-truthy-iter-0 merge_method: model_stock base_model: alpindale/Mistral-7B-v0.2-hf dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "MiniMoog/Mergerix-7b-v0.2" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
MiniMoog/Mergerix-7b-v0.3
MiniMoog
2024-04-02T20:56:00Z
739
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-04-02T20:49:46Z
--- tags: - merge - mergekit - lazymergekit license: apache-2.0 --- # Mergerix-7b-v0.3 Mergerix-7b-v0.3 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): ## 🧩 Configuration ```yaml models: - model: Gille/StrangeMerges_32-7B-slerp - model: yam-peleg/Experiment26-7B - model: liminerity/M7-7b merge_method: model_stock base_model: liminerity/M7-7b dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "MiniMoog/Mergerix-7b-v0.3" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
Changgil/K2S3-Mistral-7b-v1.43
Changgil
2024-04-05T09:33:01Z
739
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "en", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-04-05T09:27:01Z
--- license: cc-by-nc-4.0 language: - en --- --- ## Developed by : * K2S3 ## Model Number: * K2S3-Mistral-7b-v1.43 ## Base Model : * mistralai/Mistral-7B-v0.1 ### Training Data * The training data for this model includes alpaca-gpt4-data, and samples from The OpenOrca Dataset. * 이 모델의 훈련 데이터에는 alpaca-gpt4-data, 그리고 OpenOrca Dataset에서 제공한 샘플들이 포함됩니다. ### Training Method * This model was fine-tuned on the "mistralai/Mistral-7B-v0.1" base model using a full parameter tuning method with SFT (Supervised Fine-Tuning). * 이 모델은 "mistralai/Mistral-7B-v0.1" 기반 모델을 SFT를 사용하여 전체 파라미터 조정 방법으로 미세조정되었습니다. ### Hardware * Hardware: Utilized two A100 (80G*2EA) GPUs for training. * Training Factors: This model was fine-tuned with SFT, using the HuggingFace SFTtrainer and applied fsdp. * 이 모델은 SFT를 사용하여 HuggingFace SFTtrainer와 fsdp를 적용하여 미세조정되었습니다.
Nitral-AI/Nyan-Stunna-7B
Nitral-AI
2024-04-07T03:38:40Z
739
5
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "base_model:arlineka/KittyNyanster-v1", "base_model:Nitral-AI/KukulStanta-7B", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-04-05T13:25:31Z
--- base_model: - arlineka/KittyNyanster-v1 - Nitral-AI/KukulStanta-7B library_name: transformers tags: - mergekit - merge license: other --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/642265bc01c62c1e4102dc36/RXD3Lqz3_XmZ2Uk38KfaW.png) # RP "With the zoomies" # Vision/multimodal capabilities: Quants Found Here: If you want to use vision functionality: * You must use the latest versions of [Koboldcpp](https://github.com/LostRuins/koboldcpp). To use the multimodal capabilities of this model and use **vision** you need to load the specified **mmproj** file, this can be found inside this model repo. * You can load the **mmproj** by using the corresponding section in the interface: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65d4cf2693a0a3744a27536c/UX6Ubss2EPNAT3SKGMLe0.png)
ABX-AI/Silver-Sun-v2-11B
ABX-AI
2024-04-09T16:20:32Z
739
3
transformers
[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "not-for-all-audiences", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-04-08T20:36:44Z
--- base_model: [] library_name: transformers tags: - mergekit - merge - llama - not-for-all-audiences license: other --- # Silver-Sun-v2-11B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65d936ad52eca001fdcd3245/9DobeVeyL98G7QUufEeQg.png) > This is an updated version of Silver-Sun-11B. The change is that now the Solstice-FKL-v2-10.7B merge uses Sao10K/Fimbulvetr-11B-v2 instead of v1. > Additionally, the config of the original Silver-Sun was wrong, and I have also updated that. > As expected, this is a HIGHLY uncensored model. It should perform even better than the v1 due to the updated Fimb, and the fixed config. **Works with Alpaca, and from my tests, also ChatML. However Alpaca may be a better option. Try it out and use whatever works better for you.** **Due to a quirk with Solar, if you want the best quality either launch at 4K context, or launch at 8K (and possibly beyond - have not tested it that high) with 4k context pre-loaded in the prompt.** > This model is intended for fictional storytelling and writing, focusing on NSFW capabilities and lack of censorship for RP reasons. [GGUF / IQ / Imatrix](https://huggingface.co/ABX-AI/Silver-Sun-v2-11B-GGUF-IQ-Imatrix) ## Merge Details This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [Himitsui/Kaiju-11B](https://huggingface.co/Himitsui/Kaiju-11B) * ABX-AI/Solstice-FKL-v2-10.7B >[!NOTE] >A mixture of [Sao10K/Solstice-11B-v1](https://huggingface.co/Sao10K/Solstice-11B-v1) and >[ABX-AI/Fimbulvetr-Kuro-Lotus-v2-10.7B] which is updated saishf/Fimbulvetr-Kuro-Lotus-10.7B with Fimb v2 ### OpenLLM Eval Results [Detailed Results + Failed GSM8K](https://huggingface.co/datasets/open-llm-leaderboard/details_ABX-AI__Silver-Sun-v2-11B) >[!NOTE] >I had to remove GSM8K from the results and manually re-average the rest. GSM8K failed due to some issue with formatting, which is not experienced during practical usage. >By removing the GSM8K score, the average is VERY close to upstage/SOLAR-10.7B-v1.0 (74.20), which would make sense. >Feel free to ignore the actual average and use the other scores individually for reference. | Metric |Value| |---------------------------------|----:| |Avg. |74.04| |AI2 Reasoning Challenge (25-Shot)|69.88| |HellaSwag (10-Shot) |87.81| |MMLU (5-Shot) |66.74| |TruthfulQA (0-shot) |62.49| |Winogrande (5-shot) |83.27| ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: ./MODELS/Solstice-FKL-v2-10.7B layer_range: [0, 48] - model: Himitsui/Kaiju-11B layer_range: [0, 48] merge_method: slerp base_model: ./MODELS/Solstice-FKL-v2-10.7B parameters: t: - filter: self_attn value: [0.6, 0.7, 0.8, 0.9, 1] - filter: mlp value: [0.4, 0.3, 0.2, 0.1, 0] - value: 0.5 dtype: bfloat16 ```
Nitral-AI/Nyanade_Stunna-Maid-7B
Nitral-AI
2024-04-13T15:49:29Z
739
8
transformers
[ "transformers", "safetensors", "gguf", "mistral", "text-generation", "mergekit", "merge", "base_model:Nitral-AI/Nyanade-Maid-7B", "base_model:Nitral-AI/Nyan-Stunna-7B", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-04-12T01:39:15Z
--- base_model: - Nitral-AI/Nyanade-Maid-7B - Nitral-AI/Nyan-Stunna-7B library_name: transformers tags: - mergekit - merge license: other --- ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/642265bc01c62c1e4102dc36/qDMj9C-ApFgD9KfoVigyW.jpeg) # Quants thanks to Lewdiculus: https://huggingface.co/Lewdiculous/Nyanade_Stunna-Maid-7B-GGUF-IQ-Imatrix # Vision/multimodal capabilities: If you want to use vision functionality: * You must use the latest versions of [Koboldcpp](https://github.com/LostRuins/koboldcpp). To use the multimodal capabilities of this model and use **vision** you need to load the specified **mmproj** file, this can be found inside this model repo. * You can load the **mmproj** by using the corresponding section in the interface: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65d4cf2693a0a3744a27536c/UX6Ubss2EPNAT3SKGMLe0.png)
PrunaAI/llama3-42b-v0-GGUF-smashed
PrunaAI
2024-04-23T21:34:52Z
739
1
null
[ "gguf", "pruna-ai", "region:us" ]
null
2024-04-23T12:32:20Z
--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/CP4VSgck) ## This repo contains GGUF versions of the chargoddard/llama3-42b-v0 model. # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. **Frequently Asked Questions** - ***How does the compression work?*** The model is compressed with GGUF. - ***How does the model quality change?*** The quality of the model output might vary compared to the base model. - ***What is the model format?*** We use GGUF format. - ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data. - ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). # Downloading and running the models You can download the individual files from the Files & versions section. Here is a list of the different versions we provide. For more info checkout [this chart](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) and [this guide](https://www.reddit.com/r/LocalLLaMA/comments/1ba55rj/overview_of_gguf_quantization_methods/): | Quant type | Description | |------------|--------------------------------------------------------------------------------------------| | Q5_K_M | High quality, recommended. | | Q5_K_S | High quality, recommended. | | Q4_K_M | Good quality, uses about 4.83 bits per weight, recommended. | | Q4_K_S | Slightly lower quality with more space savings, recommended. | | IQ4_NL | Decent quality, slightly smaller than Q4_K_S with similar performance, recommended. | | IQ4_XS | Decent quality, smaller than Q4_K_S with similar performance, recommended. | | Q3_K_L | Lower quality but usable, good for low RAM availability. | | Q3_K_M | Even lower quality. | | IQ3_M | Medium-low quality, new method with decent performance comparable to Q3_K_M. | | IQ3_S | Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. | | Q3_K_S | Low quality, not recommended. | | IQ3_XS | Lower quality, new method with decent performance, slightly better than Q3_K_S. | | Q2_K | Very low quality but surprisingly usable. | ## How to download GGUF files ? **Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev - **Option A** - Downloading in `text-generation-webui`: - **Step 1**: Under Download Model, you can enter the model repo: PrunaAI/llama3-42b-v0-GGUF-smashed-smashed and below it, a specific filename to download, such as: phi-2.IQ3_M.gguf. - **Step 2**: Then click Download. - **Option B** - Downloading on the command line (including multiple files at once): - **Step 1**: We recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` - **Step 2**: Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download PrunaAI/llama3-42b-v0-GGUF-smashed-smashed llama3-42b-v0.IQ3_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> Alternatively, you can also download multiple files at once with a pattern: ```shell huggingface-cli download PrunaAI/llama3-42b-v0-GGUF-smashed-smashed --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download PrunaAI/llama3-42b-v0-GGUF-smashed-smashed llama3-42b-v0.IQ3_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## How to run model in GGUF format? - **Option A** - Introductory example with `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m llama3-42b-v0.IQ3_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<s>[INST] {prompt\} [/INST]" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) - **Option B** - Running in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20-%20Model%20Tab.md#llamacpp). - **Option C** - Running from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./llama3-42b-v0.IQ3_M.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "<s>[INST] {prompt} [/INST]", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./llama3-42b-v0.IQ3_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` - **Option D** - Running with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
Ramikan-BR/tinyllama-coder-py-4bit-v10
Ramikan-BR
2024-05-26T19:18:18Z
739
0
transformers
[ "transformers", "safetensors", "gguf", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "code", "lora", "peft", "conversational", "en", "dataset:Ramikan-BR/data-oss_instruct-decontaminated_python.jsonl", "base_model:unsloth/tinyllama-chat-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-generation
2024-05-25T12:46:49Z
--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft - code - lora - peft base_model: unsloth/tinyllama-chat-bnb-4bit pipeline_tag: text-generation datasets: Ramikan-BR/data-oss_instruct-decontaminated_python.jsonl --- # Uploaded model - **Developed by:** Ramikan-BR - **Model type:** [text-generation/Python Coder] - **Language(s) (NLP):** [en] - **License:** apache-2.0 - **Finetuned from model :** unsloth/tinyllama-chat-bnb-4bit ### Model Description <!-- Provide a longer summary of what this model is. --> ### Training Data datasets: [Ramikan-BR/data-oss_instruct-decontaminated_python.jsonl](https://huggingface.co/datasets/Ramikan-BR/data-oss_instruct-decontaminated_python.jsonl) ### Training Procedure The model was refined using [Unsloath](https://github.com/unslothai/unsloth). The dataset [ise-uiuc/Magicoder-OSS-Instruct-75K](https://huggingface.co/datasets/ise-uiuc/Magicoder-OSS-Instruct-75K/blob/main/data-oss_instruct-decontaminated.jsonl) was adjusted, leaving only data on python and divided into 10 parts, each refinement occurred for 2 epochs, using adafactor optimizer or adamw_8bit (adafactor seems to deliver less loss). ### Model Sources [optional] base_model: [unsloth/tinyllama-chat-bnb-4bit](https://huggingface.co/unsloth/tinyllama-chat-bnb-4bit) model: [Ramikan-BR/tinyllama-coder-py-4bit-v10](https://huggingface.co/Ramikan-BR/tinyllama-coder-py-4bit-v10) gguf_f16: [tinyllama-coder-py-4bit-v10-unsloth.F16.gguf](https://huggingface.co/Ramikan-BR/tinyllama-coder-py-4bit-v10/blob/main/tinyllama-coder-py-4bit-v10-unsloth.F16.gguf) gguf_Q4_K_M: [tinyllama-coder-py-4bit-v10-unsloth.Q4_K_M.gguf](https://huggingface.co/Ramikan-BR/tinyllama-coder-py-4bit-v10/blob/main/tinyllama-coder-py-4bit-v10-unsloth.Q4_K_M.gguf) gguf_Q8_0: [tinyllama-coder-py-4bit-v10-unsloth.Q8_0.gguf](https://huggingface.co/Ramikan-BR/tinyllama-coder-py-4bit-v10/blob/main/tinyllama-coder-py-4bit-v10-unsloth.Q8_0.gguf) #### Training Hyperparameters Notebook [Unsloath](https://github.com/unslothai/unsloth) that I used for AI refinement: [TinyLlama](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing) ```python %%capture # Installs Unsloth, Xformers (Flash Attention) and all other packages! !pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git" !pip install --no-deps xformers trl peft accelerate bitsandbytes # xformers "xformers<0.0.26" import os from google.colab import drive drive.mount('/content/drive') from unsloth import FastLanguageModel import torch max_seq_length = 4096 # Choose any! We auto support RoPE Scaling internally! dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+ load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False. # 4bit pre quantized models we support for 4x faster downloading + no OOMs. fourbit_models = [ "unsloth/mistral-7b-bnb-4bit", "unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "unsloth/llama-2-7b-bnb-4bit", "unsloth/llama-2-13b-bnb-4bit", "unsloth/codellama-34b-bnb-4bit", "unsloth/tinyllama-bnb-4bit", "unsloth/gemma-7b-bnb-4bit", # New Google 6 trillion tokens model 2.5x faster! "unsloth/gemma-2b-bnb-4bit", ] # More models at https://huggingface.co/unsloth model, tokenizer = FastLanguageModel.from_pretrained( model_name = "Ramikan-BR/tinyllama-coder-py-4bit_LORA-v9", # "unsloth/tinyllama" for 16bit loading max_seq_length = max_seq_length, dtype = dtype, load_in_4bit = load_in_4bit, # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf ) model = FastLanguageModel.get_peft_model( model, r = 256, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128 target_modules = ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj",], lora_alpha = 512, lora_dropout = 0, # Currently only supports dropout = 0 bias = "none", # Currently only supports bias = "none" use_gradient_checkpointing = True, # @@@ IF YOU GET OUT OF MEMORY - set to True @@@ random_state = 3407, use_rslora = False, # We support rank stabilized LoRA loftq_config = None, # And LoftQ ) alpaca_prompt = """Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Input: {} ### Output: {}""" EOS_TOKEN = tokenizer.eos_token def formatting_prompts_func(examples): inputs = examples["problem"] outputs = examples["solution"] texts = [] for input, output in zip(inputs, outputs): # Must add EOS_TOKEN, otherwise your generation will go on forever! text = alpaca_prompt.format(input, output) + EOS_TOKEN texts.append(text) return { "text" : texts} pass from datasets import load_dataset dataset = load_dataset('json', data_files='/content/drive/MyDrive/data-oss_instruct-py-10.jsonl', split='train') dataset = dataset.map(formatting_prompts_func, batched=True) from trl import SFTTrainer from transformers import TrainingArguments from unsloth import is_bfloat16_supported from transformers.utils import logging logging.set_verbosity_info() trainer = SFTTrainer( model = model, tokenizer = tokenizer, train_dataset = dataset, dataset_text_field = "text", max_seq_length = max_seq_length, dataset_num_proc = 2, packing = True, # Packs short sequences together to save time! args = TrainingArguments( per_device_train_batch_size = 2, gradient_accumulation_steps = 256, warmup_ratio = 0.1, num_train_epochs = 2, learning_rate = 2e-4, fp16 = not torch.cuda.is_bf16_supported(), bf16 = torch.cuda.is_bf16_supported(), logging_steps = 1, optim = "adafactor", # adamw_torch ou adamw_torch_fused +10% velocidade ou adafactor ou adamw_8bit weight_decay = 0.1, lr_scheduler_type = "linear", seed = 3407, output_dir = "outputs", ), ) trainer_stats = trainer.train() model.save_pretrained("lora_model") # Local saving tokenizer.save_pretrained("lora_model") model.push_to_hub("Ramikan-BR/tinyllama-coder-py-4bit_LORA-v10", token = "hf_...") # Online saving tokenizer.push_to_hub("Ramikan-BR/tinyllama-coder-py-4bit_LORA-v10", token = "hf_...") # Online saving # Merge to 16bit model.save_pretrained_merged("model", tokenizer, save_method = "merged_16bit",) model.push_to_hub_merged("Ramikan-BR/tinyllama-coder-py-4bit-v10", tokenizer, save_method = "merged_16bit", token = "hf_...") # Merge to 4bit if False: model.save_pretrained_merged("model", tokenizer, save_method = "merged_4bit",) if False: model.push_to_hub_merged("Ramikan-BR/tinyllama-coder-py-4bit-v10", tokenizer, save_method = "merged_4bit", token = "hf_...") # Just LoRA adapters if False: model.save_pretrained_merged("model", tokenizer, save_method = "lora",) if False: model.push_to_hub_merged("Ramikan-BR/tinyllama-coder-py-4bit-v10", tokenizer, save_method = "lora", token = "hf_...") # Save to 8bit Q8_0 model.save_pretrained_gguf("model", tokenizer,) model.push_to_hub_gguf("Ramikan-BR/tinyllama-coder-py-4bit-v10", tokenizer, token = "hf_...") # Save to 16bit GGUF model.save_pretrained_gguf("model", tokenizer, quantization_method = "f16") model.push_to_hub_gguf("Ramikan-BR/tinyllama-coder-py-4bit-v10", tokenizer, quantization_method = "f16", token = "hf_...") # Save to q4_k_m GGUF model.save_pretrained_gguf("model", tokenizer, quantization_method = "q4_k_m") model.push_to_hub_gguf("Ramikan-BR/tinyllama-coder-py-4bit-v10", tokenizer, quantization_method = "q4_k_m", token = "hf_...") Loss for 5 epochs in the last training session of the last part of the dataset: ==((====))== Unsloth - 2x faster free finetuning | Num GPUs = 1 \\ /| Num examples = 407 | Num Epochs = 5 O^O/ \_/ \ Batch size per device = 2 | Gradient Accumulation steps = 256 \ / Total batch size = 512 | Total steps = 5 "-____-" Number of trainable parameters = 201,850,880 [5/5 29:36, Epoch 3/5] Step Training Loss 1 0.568000 2 0.145300 3 0.506100 4 0.331900 5 0.276100 Quick test 1 after training the last part of the dataset: # alpaca_prompt = Copied from above FastLanguageModel.for_inference(model) # Enable native 2x faster inference inputs = tokenizer( [ alpaca_prompt.format( "Continue the fibonnaci sequence.", # instruction "1, 1, 2, 3, 5, 8", # input "", # output - leave this blank for generation! ) ], return_tensors = "pt").to("cuda") AI Response: ['<s> Below is an instruction that describes a task. Write a response that appropriately completes the request.\n### Input:\nContinue the fibonnaci sequence.\n\n### Output:\n1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 420, 787, 1444, 2881, 4765, 8640'] Quick test 2 after training the last part of the dataset: # alpaca_prompt = Copied from above FastLanguageModel.for_inference(model) # Enable native 2x faster inference inputs = tokenizer( [ alpaca_prompt.format( "Continue the fibonnaci sequence.", # instruction "1, 1, 2, 3, 5, 8", # input "", # output - leave this blank for generation! ) ], return_tensors = "pt").to("cuda") from transformers import TextStreamer text_streamer = TextStreamer(tokenizer) _ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128) AI Response: <s> Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Input: Continue the fibonnaci sequence. ### Output: 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 420, 787, 1444, 2881, 4765, 8640, 17281, 31362, 65325, 128672, 251345, 410000, 720000, 1280000, Quick test 3 after training the last part of the dataset: if False: from unsloth import FastLanguageModel model, tokenizer = FastLanguageModel.from_pretrained( model_name = "lora_model", # YOUR MODEL YOU USED FOR TRAINING max_seq_length = max_seq_length, dtype = dtype, load_in_4bit = load_in_4bit, ) FastLanguageModel.for_inference(model) # Enable native 2x faster inference # alpaca_prompt = You MUST copy from above! inputs = tokenizer( [ alpaca_prompt.format( "What is a famous tall tower in Paris?", # instruction "", # input "", # output - leave this blank for generation! ) ], return_tensors = "pt").to("cuda") from transformers import TextStreamer text_streamer = TextStreamer(tokenizer) _ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 64) AI Response: <s> Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Input: What is a famous tall tower in Paris? ### Output: The famous tall tower in Paris is the Eiffel Tower. It is a 300-meter-tall steel tower located in the heart of Paris, France. The tower was built in 18892 and is a popular tourist attraction. It is also a symbol of the city outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True) tokenizer.batch_decode(outputs) ``` This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
stablediffusionapi/anime-xl-v8
stablediffusionapi
2024-06-19T07:25:16Z
739
0
diffusers
[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]
text-to-image
2024-06-19T07:20:46Z
--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # anime-xl-v8 API Inference ![generated from modelslab.com](https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/7543718901718781343.png) ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change **model_id** to "anime-xl-v8" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com) Try model for free: [Generate Images](https://modelslab.com/models/anime-xl-v8) Model link: [View model](https://modelslab.com/models/anime-xl-v8) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "anime-xl-v8", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off **DMGG0RBN**
microsoft/unispeech-sat-base-plus-sv
microsoft
2021-12-17T13:56:17Z
738
0
transformers
[ "transformers", "pytorch", "unispeech-sat", "audio-xvector", "speech", "en", "arxiv:1912.07875", "arxiv:2106.06909", "arxiv:2101.00390", "arxiv:2110.05752", "endpoints_compatible", "region:us" ]
null
2022-03-02T23:29:05Z
--- language: - en tags: - speech --- # UniSpeech-SAT-Base for Speaker Verification [Microsoft's UniSpeech](https://www.microsoft.com/en-us/research/publication/unispeech-unified-speech-representation-learning-with-labeled-and-unlabeled-data/) The model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz. The model was pre-trained on: - 60,000 hours of [Libri-Light](https://arxiv.org/abs/1912.07875) - 10,000 hours of [GigaSpeech](https://arxiv.org/abs/2106.06909) - 24,000 hours of [VoxPopuli](https://arxiv.org/abs/2101.00390) [Paper: UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752) Authors: Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu **Abstract** *Self-supervised learning (SSL) is a long-standing goal for speech processing, since it utilizes large-scale unlabeled data and avoids extensive human labeling. Recent years witness great successes in applying self-supervised learning in speech recognition, while limited exploration was attempted in applying SSL for modeling speaker characteristics. In this paper, we aim to improve the existing SSL framework for speaker representation learning. Two methods are introduced for enhancing the unsupervised speaker information extraction. First, we apply the multi-task learning to the current SSL framework, where we integrate the utterance-wise contrastive loss with the SSL objective function. Second, for better speaker discrimination, we propose an utterance mixing strategy for data augmentation, where additional overlapped utterances are created unsupervisely and incorporate during training. We integrate the proposed methods into the HuBERT framework. Experiment results on SUPERB benchmark show that the proposed system achieves state-of-the-art performance in universal representation learning, especially for speaker identification oriented tasks. An ablation study is performed verifying the efficacy of each proposed method. Finally, we scale up training dataset to 94 thousand hours public audio data and achieve further performance improvement in all SUPERB tasks..* The original model can be found under https://github.com/microsoft/UniSpeech/tree/main/UniSpeech-SAT. # Fine-tuning details The model is fine-tuned on the [VoxCeleb1 dataset](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox1.html) using an X-Vector head with an Additive Margin Softmax loss [X-Vectors: Robust DNN Embeddings for Speaker Recognition](https://www.danielpovey.com/files/2018_icassp_xvectors.pdf) # Usage ## Speaker Verification ```python from transformers import Wav2Vec2FeatureExtractor, UniSpeechSatForXVector from datasets import load_dataset import torch dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation") feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained('microsoft/unispeech-sat-base-plus-sv') model = UniSpeechSatForXVector.from_pretrained('microsoft/unispeech-sat-base-plus-sv') # audio files are decoded on the fly inputs = feature_extractor(dataset[:2]["audio"]["array"], return_tensors="pt") embeddings = model(**inputs).embeddings embeddings = torch.nn.functional.normalize(embeddings, dim=-1).cpu() # the resulting embeddings can be used for cosine similarity-based retrieval cosine_sim = torch.nn.CosineSimilarity(dim=-1) similarity = cosine_sim(embeddings[0], embeddings[1]) threshold = 0.89 # the optimal threshold is dataset-dependent if similarity < threshold: print("Speakers are not the same!") ``` # License The official license can be found [here](https://github.com/microsoft/UniSpeech/blob/main/LICENSE) ![design](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/UniSpeechSAT.png)
azale-ai/DukunLM-7B-V1.0-Uncensored
azale-ai
2023-08-14T03:06:03Z
738
5
transformers
[ "transformers", "pytorch", "llama", "text-generation", "qlora", "wizardlm", "uncensored", "instruct", "chat", "alpaca", "indonesia", "id", "en", "dataset:MBZUAI/Bactrian-X", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-08-13T03:55:26Z
--- license: cc-by-nc-4.0 datasets: - MBZUAI/Bactrian-X language: - id - en tags: - qlora - wizardlm - uncensored - instruct - chat - alpaca - indonesia --- # DukunLM V1.0 - Indonesian Language Model 🧙‍♂️ 🚀 Welcome to the DukunLM V1.0 repository! DukunLM V1.0 is an open-source language model trained to generate Indonesian text using the power of AI. DukunLM, meaning "WizardLM" in Indonesian, is here to revolutionize language generation 🌟. This is an updated version from [azale-ai/DukunLM-Uncensored-7B](https://huggingface.co/azale-ai/DukunLM-Uncensored-7B) with full model release, not only adapter model like before 👽. ## Model Details | Name Model | Parameters | Google Colab | Base Model | Dataset | Prompt Format | Fine Tune Method | Sharded Version | |----------------------------------------------------------------------------------|------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------|--------------------------------------------------------|--------------------------------------------|--------------------------------------------| | [DukunLM-7B-V1.0-Uncensored](https://huggingface.co/azale-ai/DukunLM-7B-V1.0-Uncensored) | 7B | [Link](https://colab.research.google.com/drive/1UEiRqkfU1jGVMM9we4X3ooN1kkNGtfLd) | [ehartford/WizardLM-7B-V1.0-Uncensored](https://huggingface.co/ehartford/WizardLM-7B-V1.0-Uncensored) | [MBZUAI/Bactrian-X (Indonesian subset)](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/id/train) | [Alpaca](https://github.com/tatsu-lab/stanford_alpaca) | [QLoRA](https://github.com/artidoro/qlora) | [Link](https://huggingface.co/azale-ai/DukunLM-7B-V1.0-Uncensored-sharded) | | [DukunLM-13B-V1.0-Uncensored](https://huggingface.co/azale-ai/DukunLM-13B-V1.0-Uncensored) | 13B | [Link](https://colab.research.google.com/drive/19xXYcAwVFLSItHm__GhPTYMryOGjdFkF) | [ehartford/WizardLM-13B-V1.0-Uncensored](https://huggingface.co/ehartford/WizardLM-13B-V1.0-Uncensored) | [MBZUAI/Bactrian-X (Indonesian subset)](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/id/train) | [Alpaca](https://github.com/tatsu-lab/stanford_alpaca) | [QLoRA](https://github.com/artidoro/qlora) | [Link](https://huggingface.co/azale-ai/DukunLM-13B-V1.0-Uncensored-sharded) | ⚠️ **Warning**: DukunLM is an uncensored model without filters or alignment. Please use it responsibly as it may contain errors, cultural biases, and potentially offensive content. ⚠️ ## Installation To use DukunLM, ensure that PyTorch has been installed and that you have an Nvidia GPU (or use Google Colab). After that you need to install the required dependencies: ```bash pip3 install -U git+https://github.com/huggingface/transformers.git pip3 install -U git+https://github.com/huggingface/peft.git pip3 install -U git+https://github.com/huggingface/accelerate.git pip3 install -U bitsandbytes==0.39.0 einops==0.6.1 sentencepiece ``` ## How to Use ### Normal Model #### Stream Output ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer model = AutoModelForCausalLM.from_pretrained("azale-ai/DukunLM-7B-V1.0-Uncensored", torch_dtype=torch.float16).to("cuda") tokenizer = AutoTokenizer.from_pretrained("azale-ai/DukunLM-7B-V1.0-Uncensored") streamer = TextStreamer(tokenizer) instruction_prompt = "Jelaskan mengapa air penting bagi kehidupan manusia." input_prompt = "" if not input_prompt: prompt = """Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {instruction} ### Response: """ prompt = prompt.format(instruction=instruction_prompt) else: prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. ### Instruction: {instruction} ### Input: {input} ### Response: """ prompt = prompt.format(instruction=instruction_prompt, input=input_prompt) inputs = tokenizer(prompt, return_tensors="pt").to("cuda") _ = model.generate( inputs=inputs.input_ids, streamer=streamer, pad_token_id=tokenizer.pad_token_id, eos_token_id=tokenizer.eos_token_id, max_length=2048, temperature=0.7, do_sample=True, top_k=4, top_p=0.95 ) ``` #### No Stream Output ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("azale-ai/DukunLM-7B-V1.0-Uncensored", torch_dtype=torch.float16).to("cuda") tokenizer = AutoTokenizer.from_pretrained("azale-ai/DukunLM-7B-V1.0-Uncensored") instruction_prompt = "Jelaskan mengapa air penting bagi kehidupan manusia." input_prompt = "" if not input_prompt: prompt = """Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {instruction} ### Response: """ prompt = prompt.format(instruction=instruction_prompt) else: prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. ### Instruction: {instruction} ### Input: {input} ### Response: """ prompt = prompt.format(instruction=instruction_prompt, input=input_prompt) inputs = tokenizer(prompt, return_tensors="pt").to("cuda") outputs = model.generate( inputs=inputs.input_ids, pad_token_id=tokenizer.pad_token_id, eos_token_id=tokenizer.eos_token_id, max_length=2048, temperature=0.7, do_sample=True, top_k=4, top_p=0.95 ) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` ### Quantize Model #### Stream Output ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextStreamer model = AutoModelForCausalLM.from_pretrained( "azale-ai/DukunLM-7B-V1.0-Uncensored-sharded", load_in_4bit=True, torch_dtype=torch.float32, quantization_config=BitsAndBytesConfig( load_in_4bit=True, llm_int8_threshold=6.0, llm_int8_has_fp16_weight=False, bnb_4bit_compute_dtype=torch.float16, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4", ) ) tokenizer = AutoTokenizer.from_pretrained("azale-ai/DukunLM-7B-V1.0-Uncensored-sharded") streamer = TextStreamer(tokenizer) instruction_prompt = "Jelaskan mengapa air penting bagi kehidupan manusia." input_prompt = "" if not input_prompt: prompt = """Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {instruction} ### Response: """ prompt = prompt.format(instruction=instruction_prompt) else: prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. ### Instruction: {instruction} ### Input: {input} ### Response: """ prompt = prompt.format(instruction=instruction_prompt, input=input_prompt) inputs = tokenizer(prompt, return_tensors="pt").to("cuda") _ = model.generate( inputs=inputs.input_ids, streamer=streamer, pad_token_id=tokenizer.pad_token_id, eos_token_id=tokenizer.eos_token_id, max_length=2048, temperature=0.7, do_sample=True, top_k=4, top_p=0.95 ) ``` #### No Stream Output ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig model = AutoModelForCausalLM.from_pretrained( "azale-ai/DukunLM-7B-V1.0-Uncensored-sharded", load_in_4bit=True, torch_dtype=torch.float32, quantization_config=BitsAndBytesConfig( load_in_4bit=True, llm_int8_threshold=6.0, llm_int8_has_fp16_weight=False, bnb_4bit_compute_dtype=torch.float16, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4", ) ) tokenizer = AutoTokenizer.from_pretrained("azale-ai/DukunLM-7B-V1.0-Uncensored-sharded") instruction_prompt = "Jelaskan mengapa air penting bagi kehidupan manusia." input_prompt = "" if not input_prompt: prompt = """Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {instruction} ### Response: """ prompt = prompt.format(instruction=instruction_prompt) else: prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. ### Instruction: {instruction} ### Input: {input} ### Response: """ prompt = prompt.format(instruction=instruction_prompt, input=input_prompt) inputs = tokenizer(prompt, return_tensors="pt").to("cuda") outputs = model.generate( inputs=inputs.input_ids, pad_token_id=tokenizer.pad_token_id, eos_token_id=tokenizer.eos_token_id, max_length=2048, temperature=0.7, do_sample=True, top_k=4, top_p=0.95 ) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` ## Benchmark Coming soon, stay tune 🙂🙂. ## Limitations - The base model language is English and fine-tuned to Indonesia - Cultural and contextual biases ## License DukunLM V1.0 is licensed under the [Creative Commons NonCommercial (CC BY-NC 4.0) license](https://creativecommons.org/licenses/by-nc/4.0/legalcode). ## Contributing We welcome contributions to enhance and improve DukunLM V1.0. If you have any suggestions or find any issues, please feel free to open an issue or submit a pull request. Also we're open to sponsor for compute power. ## Contact Us [[email protected]](mailto:[email protected])
umd-zhou-lab/claude2-alpaca-13B
umd-zhou-lab
2023-10-22T16:07:55Z
738
5
transformers
[ "transformers", "pytorch", "llama", "text-generation", "en", "dataset:umd-zhou-lab/claude2_alpaca", "license:llama2", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-09-18T22:23:37Z
--- license: llama2 datasets: - umd-zhou-lab/claude2_alpaca language: - en --- # Model Card for umd-zhou-lab/claude2-alpaca-13B <!-- Provide a quick summary of what the model is/does. --> This model is trained by fine-tuning llama-2 with claude2 alpaca data. ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** UMD Tianyi Zhou Lab - **Model type:** An auto-regressive language model based on the transformer architecture - **License:** Llama 2 Community License Agreement - **Finetuned from model:** [meta-llama/Llama-2-13b](https://huggingface.co/meta-llama/Llama-2-13b) ### Model Sources <!-- Provide the basic links for the model. --> - **GitHub:** [Claude2-Alpaca](https://github.com/Lichang-Chen/claude2-alpaca) - **Data:** [claude2_alpaca](https://huggingface.co/datasets/umd-zhou-lab/claude2_alpaca) ## Uses The primary use of this model is research on large language models and chatbots. The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence. ## Training We use the prompt from [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) | Hyperparameter | Global Batch Size | Learning rate | Epochs | Max length | Weight decay | | --- | ---: | ---: | ---: | ---: | ---: | | Model (13B) | 128 | 1e-5 | 5 | 2048 | 0 | ## Performance Compared to the llama2-chat, our models can have better average performance.<br> | | Average | ARC | HellaSwag | MMLU | TruthfulQA | Alpaca_Eval | Avg Length | |---|---|---|---|---|---|---|---| | Llama-2-7b-chat | 56.335 | 52.9 | 78.55 | 48.32 | 45.57 | 71.37 | 1479 | | Llama-2-13b-chat | 59.935 | 59.04| 81.94 | 54.64 | 44.12 | 81.09 | 1513 | ||||||||| | claude_alpaca-7b | 57.78 | 56.66 | 81.17 | 46.58 | 46.71 | 71.23 | 1066 | | claude_alpaca-13b | 61.29 | 61.18 | 84.08 | 55.74 | 44.18 | 78.93 | 1127 | ## Citation Please consider citing our paper if you think our codes, data, or models are useful. Thank you! ``` @misc{claude2-alpaca, author = {Lichang Chen and Khalid Saifullah and Ming Li and Tianyi Zhou and Heng Huang}, title = {Claude2-Alpaca: Instruction tuning datasets distilled from claude}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/Lichang-Chen/claude2-alpaca}}, } ```
Yntec/Darkside
Yntec
2023-09-29T07:52:19Z
738
2
diffusers
[ "diffusers", "safetensors", "Anime", "Horror", "Pixar", "DucHaiten", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-09-29T06:54:29Z
--- license: creativeml-openrail-m library_name: diffusers pipeline_tag: text-to-image tags: - Anime - Horror - Pixar - DucHaiten - stable-diffusion - stable-diffusion-diffusers - diffusers - text-to-image --- # DucHaiten Darkside fp16 no-ema version of this model: https://civitai.com/models/5426?modelVersionId=6311 Samples and prompt: ![Sample](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/IJYod8CehiODd6XqdoJFg.png) ![Sample](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/Obn05NKzFtkJq4kTcqBos.png) Cartoon Pretty CUTE Girl, ilya kuvshinov detailed, DETAILED CHIBI EYES, gorgeous detailed hair, high school, Magazine ad, iconic, 1949, sharp focus. visible brushstrokes By KlaysMoji and artgerm and Clay Mann and and simon cowell and leyendecker. By Dave Rapoza. Pretty CUTE girl.
jeff31415/TinyLlama-1.1B-1.5T-OpenOrca-Alpha
jeff31415
2023-10-25T13:49:33Z
738
4
transformers
[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "en", "dataset:Open-Orca/OpenOrca", "dataset:bigcode/starcoderdata", "dataset:cerebras/SlimPajama-627B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-10-24T04:09:49Z
--- license: apache-2.0 datasets: - Open-Orca/OpenOrca - bigcode/starcoderdata - cerebras/SlimPajama-627B language: - en --- [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) #### Base model: https://huggingface.co/TinyLlama/tinyLlama-intermediate-checkpoints/tree/step-720k-token-1510B This fine tune was done on the "early" version of tinyllama-1.5T which suffers from a bug in dataset processing. See https://github.com/jzhang38/TinyLlama/issues/67. Through it suffers from the glitch, its performance seems not being damaged and still showing improvement(metrics needed) #### Dataset: Fine tuned on OpenOrca GPT4 subset for 1 epoch,Using CHATML format #### Model License: Apache 2.0, following the TinyLlama base model. #### Quantisation: GGUF format:https://huggingface.co/s3nh/jeff31415-TinyLlama-1.1B-1.5T-OpenOrca-Alpha-GGUF #### Hardware and training details: Hardware: 1*RTX A5000, ~16 hours to complete 1 epoch. GPU from autodl.com, cost around $3 for this finetuning. https://wandb.ai/jeff200402/TinyLlama-1.5T-alpha-Orca?workspace= for more details.
hf-audio/wav2vec2-bert-CV16-en
hf-audio
2024-01-15T11:41:00Z
738
4
transformers
[ "transformers", "tensorboard", "safetensors", "wav2vec2-bert", "automatic-speech-recognition", "mozilla-foundation/common_voice_16_0", "generated_from_trainer", "en", "base_model:ylacombe/w2v-bert-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2024-01-05T19:32:09Z
--- language: - en base_model: ylacombe/w2v-bert-2.0 tags: - automatic-speech-recognition - mozilla-foundation/common_voice_16_0 - generated_from_trainer metrics: - wer model-index: - name: wav2vec2-bert-CV16-en 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. --> # wav2vec2-bert-CV16-en This model is a fine-tuned version of [ylacombe/w2v-bert-2.0](https://huggingface.co/ylacombe/w2v-bert-2.0) on the MOZILLA-FOUNDATION/COMMON_VOICE_16_0 - EN dataset. It achieves the following results on the evaluation set: - Loss: 0.2427 - Wer: 0.1455 - Cer: 0.0580 ## 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: 12 - eval_batch_size: 12 - seed: 42 - distributed_type: multi-GPU - num_devices: 3 - total_train_batch_size: 36 - total_eval_batch_size: 36 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:| | 2.9554 | 0.01 | 250 | 3.1731 | 0.9999 | 0.9942 | | 2.7058 | 0.02 | 500 | 2.6717 | 1.0307 | 0.7486 | | 0.9641 | 0.02 | 750 | 0.9895 | 0.6091 | 0.2035 | | 0.6935 | 0.03 | 1000 | 0.7740 | 0.4821 | 0.1562 | | 0.617 | 0.04 | 1250 | 0.6751 | 0.4008 | 0.1303 | | 0.4826 | 0.05 | 1500 | 0.5920 | 0.3499 | 0.1170 | | 0.4252 | 0.06 | 1750 | 0.5659 | 0.3056 | 0.1053 | | 0.472 | 0.07 | 2000 | 0.5066 | 0.2869 | 0.1007 | | 0.4042 | 0.07 | 2250 | 0.4604 | 0.2662 | 0.0950 | | 0.4279 | 0.08 | 2500 | 0.5165 | 0.2587 | 0.0948 | | 0.3586 | 0.09 | 2750 | 0.4440 | 0.2461 | 0.0895 | | 0.2715 | 0.1 | 3000 | 0.5096 | 0.2468 | 0.0904 | | 0.413 | 0.11 | 3250 | 0.4416 | 0.2350 | 0.0879 | | 0.3142 | 0.11 | 3500 | 0.4591 | 0.2280 | 0.0856 | | 0.286 | 0.12 | 3750 | 0.4529 | 0.2284 | 0.0860 | | 0.3112 | 0.13 | 4000 | 0.4621 | 0.2320 | 0.0875 | | 0.3294 | 0.14 | 4250 | 0.4528 | 0.2294 | 0.0862 | | 0.3522 | 0.15 | 4500 | 0.4279 | 0.2287 | 0.0861 | | 0.2977 | 0.15 | 4750 | 0.4403 | 0.2200 | 0.0830 | | 0.2391 | 0.16 | 5000 | 0.4360 | 0.2161 | 0.0831 | | 0.3025 | 0.17 | 5250 | 0.4214 | 0.2157 | 0.0831 | | 0.309 | 0.18 | 5500 | 0.4060 | 0.2125 | 0.0818 | | 0.2872 | 0.19 | 5750 | 0.4233 | 0.2189 | 0.0824 | | 0.2796 | 0.2 | 6000 | 0.4055 | 0.2151 | 0.0823 | | 0.2609 | 0.2 | 6250 | 0.4374 | 0.2194 | 0.0853 | | 0.283 | 0.21 | 6500 | 0.4288 | 0.2215 | 0.0877 | | 0.3028 | 0.22 | 6750 | 0.4180 | 0.2166 | 0.0837 | | 0.2565 | 0.23 | 7000 | 0.4476 | 0.2268 | 0.0892 | | 0.2824 | 0.24 | 7250 | 0.4057 | 0.2195 | 0.0850 | | 0.325 | 0.24 | 7500 | 0.3926 | 0.2157 | 0.0849 | | 0.336 | 0.25 | 7750 | 0.4469 | 0.2208 | 0.0879 | | 0.304 | 0.26 | 8000 | 0.4292 | 0.2245 | 0.0886 | | 0.2457 | 0.27 | 8250 | 0.4198 | 0.2204 | 0.0856 | | 0.2768 | 0.28 | 8500 | 0.4330 | 0.2184 | 0.0859 | | 0.2165 | 0.29 | 8750 | 0.4276 | 0.2173 | 0.0864 | | 0.3015 | 0.29 | 9000 | 0.4255 | 0.2223 | 0.0882 | | 0.308 | 0.3 | 9250 | 0.4356 | 0.2318 | 0.0925 | | 0.2981 | 0.31 | 9500 | 0.4514 | 0.2226 | 0.0884 | | 0.2944 | 0.32 | 9750 | 0.4182 | 0.2293 | 0.0901 | | 0.3298 | 0.33 | 10000 | 0.4290 | 0.2275 | 0.0892 | | 0.2523 | 0.33 | 10250 | 0.4032 | 0.2191 | 0.0865 | | 0.2887 | 0.34 | 10500 | 0.4218 | 0.2284 | 0.0917 | | 0.3156 | 0.35 | 10750 | 0.3930 | 0.2271 | 0.0898 | | 0.2526 | 0.36 | 11000 | 0.4367 | 0.2304 | 0.0928 | | 0.2561 | 0.37 | 11250 | 0.4261 | 0.2279 | 0.0916 | | 0.2291 | 0.37 | 11500 | 0.4401 | 0.2231 | 0.0899 | | 0.2521 | 0.38 | 11750 | 0.4101 | 0.2232 | 0.0895 | | 0.2249 | 0.39 | 12000 | 0.4021 | 0.2270 | 0.0913 | | 0.2917 | 0.4 | 12250 | 0.4124 | 0.2267 | 0.0915 | | 0.2436 | 0.41 | 12500 | 0.4197 | 0.2257 | 0.0903 | | 0.2976 | 0.42 | 12750 | 0.3951 | 0.2230 | 0.0896 | | 0.2333 | 0.42 | 13000 | 0.4099 | 0.2250 | 0.0901 | | 0.2261 | 0.43 | 13250 | 0.4328 | 0.2168 | 0.0876 | | 0.2514 | 0.44 | 13500 | 0.3947 | 0.2208 | 0.0895 | | 0.296 | 0.45 | 13750 | 0.3953 | 0.2149 | 0.0859 | | 0.2426 | 0.46 | 14000 | 0.3831 | 0.2119 | 0.0852 | | 0.2258 | 0.46 | 14250 | 0.4060 | 0.2263 | 0.0915 | | 0.2565 | 0.47 | 14500 | 0.4057 | 0.2237 | 0.0901 | | 0.2834 | 0.48 | 14750 | 0.4112 | 0.2167 | 0.0876 | | 0.234 | 0.49 | 15000 | 0.3802 | 0.2133 | 0.0852 | | 0.3084 | 0.5 | 15250 | 0.3837 | 0.2151 | 0.0871 | | 0.3051 | 0.51 | 15500 | 0.3848 | 0.2145 | 0.0867 | | 0.2364 | 0.51 | 15750 | 0.3817 | 0.2134 | 0.0870 | | 0.2345 | 0.52 | 16000 | 0.3883 | 0.2163 | 0.0874 | | 0.2235 | 0.53 | 16250 | 0.3740 | 0.2136 | 0.0869 | | 0.2365 | 0.54 | 16500 | 0.3711 | 0.2112 | 0.0850 | | 0.2449 | 0.55 | 16750 | 0.3805 | 0.2127 | 0.0858 | | 0.2569 | 0.55 | 17000 | 0.3794 | 0.2124 | 0.0863 | | 0.2273 | 0.56 | 17250 | 0.3922 | 0.2207 | 0.0895 | | 0.2492 | 0.57 | 17500 | 0.3670 | 0.2195 | 0.0874 | | 0.236 | 0.58 | 17750 | 0.3799 | 0.2120 | 0.0862 | | 0.2823 | 0.59 | 18000 | 0.3734 | 0.2144 | 0.0867 | | 0.2349 | 0.59 | 18250 | 0.3972 | 0.2175 | 0.0889 | | 0.2156 | 0.6 | 18500 | 0.3729 | 0.2157 | 0.0867 | | 0.2812 | 0.61 | 18750 | 0.3905 | 0.2117 | 0.0854 | | 0.242 | 0.62 | 19000 | 0.3912 | 0.2114 | 0.0855 | | 0.2237 | 0.63 | 19250 | 0.3794 | 0.2155 | 0.0877 | | 0.255 | 0.64 | 19500 | 0.3770 | 0.2079 | 0.0840 | | 0.1899 | 0.64 | 19750 | 0.3796 | 0.2145 | 0.0868 | | 0.2793 | 0.65 | 20000 | 0.3784 | 0.2145 | 0.0863 | | 0.2099 | 0.66 | 20250 | 0.3956 | 0.2161 | 0.0875 | | 0.22 | 0.67 | 20500 | 0.3804 | 0.2135 | 0.0875 | | 0.2213 | 0.68 | 20750 | 0.3803 | 0.2100 | 0.0849 | | 0.245 | 0.68 | 21000 | 0.3783 | 0.2142 | 0.0870 | | 0.2188 | 0.69 | 21250 | 0.3873 | 0.2163 | 0.0861 | | 0.2613 | 0.7 | 21500 | 0.3646 | 0.2105 | 0.0844 | | 0.1907 | 0.71 | 21750 | 0.3830 | 0.2101 | 0.0853 | | 0.2095 | 0.72 | 22000 | 0.3794 | 0.2087 | 0.0849 | | 0.2319 | 0.73 | 22250 | 0.3548 | 0.2087 | 0.0842 | | 0.2049 | 0.73 | 22500 | 0.3782 | 0.2075 | 0.0837 | | 0.2248 | 0.74 | 22750 | 0.3736 | 0.2100 | 0.0845 | | 0.2277 | 0.75 | 23000 | 0.3712 | 0.2105 | 0.0845 | | 0.2115 | 0.76 | 23250 | 0.3722 | 0.2124 | 0.0859 | | 0.2001 | 0.77 | 23500 | 0.3602 | 0.2072 | 0.0832 | | 0.2095 | 0.77 | 23750 | 0.3607 | 0.2106 | 0.0851 | | 0.2286 | 0.78 | 24000 | 0.3810 | 0.2132 | 0.0876 | | 0.2284 | 0.79 | 24250 | 0.3677 | 0.2066 | 0.0847 | | 0.2003 | 0.8 | 24500 | 0.3650 | 0.2098 | 0.0847 | | 0.1992 | 0.81 | 24750 | 0.3491 | 0.2019 | 0.0813 | | 0.224 | 0.81 | 25000 | 0.3602 | 0.2043 | 0.0825 | | 0.2181 | 0.82 | 25250 | 0.3712 | 0.2120 | 0.0867 | | 0.2226 | 0.83 | 25500 | 0.3657 | 0.2028 | 0.0830 | | 0.1912 | 0.84 | 25750 | 0.3662 | 0.2076 | 0.0846 | | 0.2283 | 0.85 | 26000 | 0.3505 | 0.2049 | 0.0825 | | 0.2068 | 0.86 | 26250 | 0.3622 | 0.2111 | 0.0852 | | 0.2444 | 0.86 | 26500 | 0.3660 | 0.2055 | 0.0840 | | 0.2055 | 0.87 | 26750 | 0.3625 | 0.2055 | 0.0830 | | 0.2074 | 0.88 | 27000 | 0.3566 | 0.1981 | 0.0812 | | 0.2019 | 0.89 | 27250 | 0.3537 | 0.2038 | 0.0822 | | 0.2174 | 0.9 | 27500 | 0.3664 | 0.1990 | 0.0809 | | 0.2009 | 0.9 | 27750 | 0.3512 | 0.2035 | 0.0821 | | 0.211 | 0.91 | 28000 | 0.3707 | 0.2068 | 0.0846 | | 0.2541 | 0.92 | 28250 | 0.3435 | 0.1992 | 0.0812 | | 0.2108 | 0.93 | 28500 | 0.3461 | 0.2046 | 0.0828 | | 0.2274 | 0.94 | 28750 | 0.3364 | 0.1998 | 0.0812 | | 0.2175 | 0.95 | 29000 | 0.3742 | 0.2113 | 0.0864 | | 0.2368 | 0.95 | 29250 | 0.3431 | 0.2051 | 0.0833 | | 0.1831 | 0.96 | 29500 | 0.3468 | 0.2034 | 0.0825 | | 0.2202 | 0.97 | 29750 | 0.3342 | 0.1964 | 0.0791 | | 0.183 | 0.98 | 30000 | 0.3413 | 0.1966 | 0.0792 | | 0.1958 | 0.99 | 30250 | 0.3466 | 0.1991 | 0.0809 | | 0.2167 | 0.99 | 30500 | 0.3530 | 0.2024 | 0.0816 | | 0.2057 | 1.0 | 30750 | 0.3334 | 0.1960 | 0.0788 | | 0.1982 | 1.01 | 31000 | 0.3312 | 0.1951 | 0.0789 | | 0.2123 | 1.02 | 31250 | 0.3285 | 0.1955 | 0.0785 | | 0.2269 | 1.03 | 31500 | 0.3548 | 0.2034 | 0.0812 | | 0.2056 | 1.03 | 31750 | 0.3433 | 0.1969 | 0.0793 | | 0.2234 | 1.04 | 32000 | 0.3446 | 0.1981 | 0.0805 | | 0.1913 | 1.05 | 32250 | 0.3465 | 0.1969 | 0.0792 | | 0.2005 | 1.06 | 32500 | 0.3348 | 0.1947 | 0.0784 | | 0.2017 | 1.07 | 32750 | 0.3567 | 0.1972 | 0.0796 | | 0.2523 | 1.08 | 33000 | 0.3367 | 0.1971 | 0.0801 | | 0.1716 | 1.08 | 33250 | 0.3476 | 0.1975 | 0.0799 | | 0.168 | 1.09 | 33500 | 0.3346 | 0.1951 | 0.0790 | | 0.1995 | 1.1 | 33750 | 0.3564 | 0.1971 | 0.0794 | | 0.198 | 1.11 | 34000 | 0.3409 | 0.1988 | 0.0796 | | 0.1801 | 1.12 | 34250 | 0.3303 | 0.1995 | 0.0798 | | 0.181 | 1.12 | 34500 | 0.3363 | 0.1967 | 0.0794 | | 0.1966 | 1.13 | 34750 | 0.3375 | 0.1947 | 0.0784 | | 0.2163 | 1.14 | 35000 | 0.3441 | 0.2011 | 0.0810 | | 0.2285 | 1.15 | 35250 | 0.3303 | 0.1972 | 0.0801 | | 0.1814 | 1.16 | 35500 | 0.3462 | 0.1895 | 0.0772 | | 0.2127 | 1.17 | 35750 | 0.3393 | 0.1904 | 0.0775 | | 0.1795 | 1.17 | 36000 | 0.3374 | 0.1928 | 0.0780 | | 0.2062 | 1.18 | 36250 | 0.3286 | 0.1929 | 0.0783 | | 0.172 | 1.19 | 36500 | 0.3334 | 0.1929 | 0.0781 | | 0.1534 | 1.2 | 36750 | 0.3287 | 0.1895 | 0.0763 | | 0.2101 | 1.21 | 37000 | 0.3261 | 0.1888 | 0.0764 | | 0.2342 | 1.21 | 37250 | 0.3413 | 0.2007 | 0.0812 | | 0.1692 | 1.22 | 37500 | 0.3375 | 0.1932 | 0.0780 | | 0.165 | 1.23 | 37750 | 0.3220 | 0.1903 | 0.0767 | | 0.2067 | 1.24 | 38000 | 0.3212 | 0.1855 | 0.0754 | | 0.1984 | 1.25 | 38250 | 0.3339 | 0.1890 | 0.0762 | | 0.2117 | 1.25 | 38500 | 0.3224 | 0.1900 | 0.0761 | | 0.2036 | 1.26 | 38750 | 0.3410 | 0.1923 | 0.0790 | | 0.2072 | 1.27 | 39000 | 0.3291 | 0.1904 | 0.0770 | | 0.1962 | 1.28 | 39250 | 0.3237 | 0.1908 | 0.0770 | | 0.2055 | 1.29 | 39500 | 0.3260 | 0.1896 | 0.0767 | | 0.1753 | 1.3 | 39750 | 0.3375 | 0.1915 | 0.0777 | | 0.1983 | 1.3 | 40000 | 0.3236 | 0.1850 | 0.0750 | | 0.173 | 1.31 | 40250 | 0.3253 | 0.1870 | 0.0754 | | 0.1773 | 1.32 | 40500 | 0.3316 | 0.1923 | 0.0766 | | 0.1649 | 1.33 | 40750 | 0.3218 | 0.1842 | 0.0749 | | 0.1806 | 1.34 | 41000 | 0.3161 | 0.1907 | 0.0769 | | 0.1639 | 1.34 | 41250 | 0.3293 | 0.1898 | 0.0773 | | 0.1733 | 1.35 | 41500 | 0.3148 | 0.1881 | 0.0756 | | 0.1603 | 1.36 | 41750 | 0.3137 | 0.1851 | 0.0748 | | 0.1753 | 1.37 | 42000 | 0.3100 | 0.1857 | 0.0751 | | 0.1823 | 1.38 | 42250 | 0.3170 | 0.1821 | 0.0740 | | 0.1647 | 1.39 | 42500 | 0.3210 | 0.1863 | 0.0758 | | 0.1699 | 1.39 | 42750 | 0.3151 | 0.1861 | 0.0761 | | 0.1693 | 1.4 | 43000 | 0.3184 | 0.1800 | 0.0730 | | 0.1885 | 1.41 | 43250 | 0.3089 | 0.1830 | 0.0739 | | 0.1626 | 1.42 | 43500 | 0.3203 | 0.1852 | 0.0746 | | 0.1707 | 1.43 | 43750 | 0.3263 | 0.1834 | 0.0745 | | 0.1997 | 1.43 | 44000 | 0.3092 | 0.1807 | 0.0732 | | 0.1568 | 1.44 | 44250 | 0.3096 | 0.1828 | 0.0742 | | 0.1698 | 1.45 | 44500 | 0.3142 | 0.1826 | 0.0736 | | 0.165 | 1.46 | 44750 | 0.3205 | 0.1903 | 0.0765 | | 0.1834 | 1.47 | 45000 | 0.3226 | 0.1855 | 0.0750 | | 0.1614 | 1.47 | 45250 | 0.3141 | 0.1825 | 0.0737 | | 0.1794 | 1.48 | 45500 | 0.3100 | 0.1823 | 0.0734 | | 0.1838 | 1.49 | 45750 | 0.2988 | 0.1825 | 0.0732 | | 0.1678 | 1.5 | 46000 | 0.3061 | 0.1797 | 0.0726 | | 0.1691 | 1.51 | 46250 | 0.3192 | 0.1821 | 0.0743 | | 0.1743 | 1.52 | 46500 | 0.3133 | 0.1815 | 0.0736 | | 0.1666 | 1.52 | 46750 | 0.3034 | 0.1821 | 0.0731 | | 0.1903 | 1.53 | 47000 | 0.3016 | 0.1792 | 0.0721 | | 0.2236 | 1.54 | 47250 | 0.3137 | 0.1819 | 0.0739 | | 0.1469 | 1.55 | 47500 | 0.3092 | 0.1796 | 0.0725 | | 0.175 | 1.56 | 47750 | 0.3009 | 0.1753 | 0.0715 | | 0.1578 | 1.56 | 48000 | 0.3023 | 0.1778 | 0.0717 | | 0.1998 | 1.57 | 48250 | 0.3059 | 0.1816 | 0.0726 | | 0.1887 | 1.58 | 48500 | 0.3063 | 0.1791 | 0.0720 | | 0.1635 | 1.59 | 48750 | 0.3027 | 0.1786 | 0.0722 | | 0.2074 | 1.6 | 49000 | 0.3001 | 0.1756 | 0.0708 | | 0.1719 | 1.61 | 49250 | 0.2957 | 0.1788 | 0.0718 | | 0.1654 | 1.61 | 49500 | 0.3020 | 0.1795 | 0.0721 | | 0.1572 | 1.62 | 49750 | 0.3011 | 0.1769 | 0.0710 | | 0.177 | 1.63 | 50000 | 0.2929 | 0.1783 | 0.0712 | | 0.1771 | 1.64 | 50250 | 0.3082 | 0.1812 | 0.0722 | | 0.1796 | 1.65 | 50500 | 0.2946 | 0.1781 | 0.0713 | | 0.1585 | 1.65 | 50750 | 0.3107 | 0.1819 | 0.0733 | | 0.1815 | 1.66 | 51000 | 0.3073 | 0.1775 | 0.0721 | | 0.1601 | 1.67 | 51250 | 0.3020 | 0.1734 | 0.0701 | | 0.1769 | 1.68 | 51500 | 0.3063 | 0.1771 | 0.0710 | | 0.1786 | 1.69 | 51750 | 0.3014 | 0.1766 | 0.0703 | | 0.1743 | 1.69 | 52000 | 0.3096 | 0.1767 | 0.0710 | | 0.1864 | 1.7 | 52250 | 0.3063 | 0.1802 | 0.0720 | | 0.1399 | 1.71 | 52500 | 0.3094 | 0.1773 | 0.0718 | | 0.1678 | 1.72 | 52750 | 0.3073 | 0.1796 | 0.0724 | | 0.1497 | 1.73 | 53000 | 0.2959 | 0.1753 | 0.0701 | | 0.1565 | 1.74 | 53250 | 0.2955 | 0.1729 | 0.0690 | | 0.1942 | 1.74 | 53500 | 0.2926 | 0.1742 | 0.0693 | | 0.1848 | 1.75 | 53750 | 0.2897 | 0.1721 | 0.0686 | | 0.1664 | 1.76 | 54000 | 0.2951 | 0.1787 | 0.0708 | | 0.1296 | 1.77 | 54250 | 0.2899 | 0.1705 | 0.0679 | | 0.1763 | 1.78 | 54500 | 0.2877 | 0.1725 | 0.0686 | | 0.1627 | 1.78 | 54750 | 0.2959 | 0.1735 | 0.0697 | | 0.1433 | 1.79 | 55000 | 0.2952 | 0.1729 | 0.0693 | | 0.1632 | 1.8 | 55250 | 0.2829 | 0.1728 | 0.0686 | | 0.1837 | 1.81 | 55500 | 0.2840 | 0.1734 | 0.0688 | | 0.146 | 1.82 | 55750 | 0.2855 | 0.1686 | 0.0675 | | 0.1862 | 1.83 | 56000 | 0.2845 | 0.1721 | 0.0691 | | 0.1576 | 1.83 | 56250 | 0.2917 | 0.1762 | 0.0703 | | 0.1183 | 1.84 | 56500 | 0.2828 | 0.1713 | 0.0687 | | 0.1582 | 1.85 | 56750 | 0.2910 | 0.1708 | 0.0684 | | 0.1699 | 1.86 | 57000 | 0.2927 | 0.1733 | 0.0693 | | 0.1761 | 1.87 | 57250 | 0.2812 | 0.1687 | 0.0671 | | 0.1421 | 1.87 | 57500 | 0.2869 | 0.1699 | 0.0683 | | 0.1662 | 1.88 | 57750 | 0.2913 | 0.1748 | 0.0697 | | 0.151 | 1.89 | 58000 | 0.2984 | 0.1736 | 0.0692 | | 0.1513 | 1.9 | 58250 | 0.2862 | 0.1715 | 0.0683 | | 0.165 | 1.91 | 58500 | 0.2808 | 0.1691 | 0.0675 | | 0.1391 | 1.91 | 58750 | 0.2848 | 0.1696 | 0.0676 | | 0.1297 | 1.92 | 59000 | 0.2837 | 0.1666 | 0.0666 | | 0.1398 | 1.93 | 59250 | 0.2771 | 0.1682 | 0.0673 | | 0.1356 | 1.94 | 59500 | 0.2868 | 0.1710 | 0.0686 | | 0.1552 | 1.95 | 59750 | 0.2817 | 0.1707 | 0.0687 | | 0.1697 | 1.96 | 60000 | 0.2901 | 0.1691 | 0.0673 | | 0.1545 | 1.96 | 60250 | 0.2796 | 0.1671 | 0.0668 | | 0.1598 | 1.97 | 60500 | 0.2791 | 0.1655 | 0.0664 | | 0.147 | 1.98 | 60750 | 0.2775 | 0.1673 | 0.0665 | | 0.1658 | 1.99 | 61000 | 0.2742 | 0.1681 | 0.0665 | | 0.1501 | 2.0 | 61250 | 0.2811 | 0.1702 | 0.0676 | | 0.1428 | 2.0 | 61500 | 0.2827 | 0.1669 | 0.0665 | | 0.1107 | 2.01 | 61750 | 0.2854 | 0.1649 | 0.0664 | | 0.1176 | 2.02 | 62000 | 0.2881 | 0.1650 | 0.0661 | | 0.1032 | 2.03 | 62250 | 0.2706 | 0.1647 | 0.0657 | | 0.1229 | 2.04 | 62500 | 0.2806 | 0.1678 | 0.0666 | | 0.1397 | 2.05 | 62750 | 0.2799 | 0.1634 | 0.0654 | | 0.1182 | 2.05 | 63000 | 0.2799 | 0.1653 | 0.0662 | | 0.1294 | 2.06 | 63250 | 0.2746 | 0.1635 | 0.0656 | | 0.1358 | 2.07 | 63500 | 0.2738 | 0.1643 | 0.0653 | | 0.1464 | 2.08 | 63750 | 0.2793 | 0.1662 | 0.0664 | | 0.1362 | 2.09 | 64000 | 0.2738 | 0.1635 | 0.0650 | | 0.1264 | 2.09 | 64250 | 0.2795 | 0.1653 | 0.0666 | | 0.1189 | 2.1 | 64500 | 0.2792 | 0.1657 | 0.0658 | | 0.1157 | 2.11 | 64750 | 0.2754 | 0.1619 | 0.0648 | | 0.1232 | 2.12 | 65000 | 0.2798 | 0.1627 | 0.0646 | | 0.1209 | 2.13 | 65250 | 0.2723 | 0.1623 | 0.0654 | | 0.1079 | 2.13 | 65500 | 0.2769 | 0.1611 | 0.0644 | | 0.1217 | 2.14 | 65750 | 0.2694 | 0.1604 | 0.0642 | | 0.1207 | 2.15 | 66000 | 0.2758 | 0.1608 | 0.0645 | | 0.1515 | 2.16 | 66250 | 0.2691 | 0.1634 | 0.0654 | | 0.1145 | 2.17 | 66500 | 0.2770 | 0.1623 | 0.0651 | | 0.1752 | 2.18 | 66750 | 0.2696 | 0.1603 | 0.0642 | | 0.1366 | 2.18 | 67000 | 0.2725 | 0.1610 | 0.0647 | | 0.1171 | 2.19 | 67250 | 0.2734 | 0.1615 | 0.0647 | | 0.1429 | 2.2 | 67500 | 0.2748 | 0.1602 | 0.0644 | | 0.1329 | 2.21 | 67750 | 0.2727 | 0.1593 | 0.0643 | | 0.1126 | 2.22 | 68000 | 0.2734 | 0.1603 | 0.0645 | | 0.117 | 2.22 | 68250 | 0.2803 | 0.1632 | 0.0655 | | 0.1109 | 2.23 | 68500 | 0.2748 | 0.1589 | 0.0644 | | 0.1296 | 2.24 | 68750 | 0.2749 | 0.1594 | 0.0640 | | 0.1506 | 2.25 | 69000 | 0.2680 | 0.1582 | 0.0633 | | 0.1282 | 2.26 | 69250 | 0.2664 | 0.1587 | 0.0638 | | 0.0987 | 2.27 | 69500 | 0.2700 | 0.1593 | 0.0638 | | 0.1288 | 2.27 | 69750 | 0.2726 | 0.1582 | 0.0636 | | 0.1022 | 2.28 | 70000 | 0.2744 | 0.1577 | 0.0636 | | 0.1102 | 2.29 | 70250 | 0.2672 | 0.1586 | 0.0636 | | 0.1165 | 2.3 | 70500 | 0.2677 | 0.1569 | 0.0632 | | 0.1289 | 2.31 | 70750 | 0.2700 | 0.1558 | 0.0628 | | 0.1144 | 2.31 | 71000 | 0.2780 | 0.1585 | 0.0637 | | 0.1092 | 2.32 | 71250 | 0.2687 | 0.1561 | 0.0629 | | 0.1126 | 2.33 | 71500 | 0.2733 | 0.1553 | 0.0623 | | 0.1231 | 2.34 | 71750 | 0.2712 | 0.1568 | 0.0630 | | 0.1302 | 2.35 | 72000 | 0.2638 | 0.1548 | 0.0622 | | 0.1104 | 2.35 | 72250 | 0.2671 | 0.1561 | 0.0632 | | 0.1076 | 2.36 | 72500 | 0.2636 | 0.1552 | 0.0623 | | 0.1193 | 2.37 | 72750 | 0.2650 | 0.1580 | 0.0633 | | 0.1163 | 2.38 | 73000 | 0.2624 | 0.1555 | 0.0621 | | 0.1108 | 2.39 | 73250 | 0.2667 | 0.1552 | 0.0622 | | 0.0903 | 2.4 | 73500 | 0.2643 | 0.1555 | 0.0626 | | 0.1388 | 2.4 | 73750 | 0.2674 | 0.1546 | 0.0623 | | 0.1069 | 2.41 | 74000 | 0.2621 | 0.1553 | 0.0623 | | 0.1054 | 2.42 | 74250 | 0.2567 | 0.1549 | 0.0617 | | 0.1151 | 2.43 | 74500 | 0.2625 | 0.1538 | 0.0617 | | 0.1304 | 2.44 | 74750 | 0.2588 | 0.1537 | 0.0616 | | 0.1102 | 2.44 | 75000 | 0.2598 | 0.1533 | 0.0617 | | 0.0926 | 2.45 | 75250 | 0.2592 | 0.1529 | 0.0612 | | 0.118 | 2.46 | 75500 | 0.2565 | 0.1542 | 0.0614 | | 0.1027 | 2.47 | 75750 | 0.2603 | 0.1520 | 0.0611 | | 0.1084 | 2.48 | 76000 | 0.2603 | 0.1529 | 0.0611 | | 0.1077 | 2.49 | 76250 | 0.2572 | 0.1519 | 0.0610 | | 0.1033 | 2.49 | 76500 | 0.2595 | 0.1518 | 0.0608 | | 0.1071 | 2.5 | 76750 | 0.2562 | 0.1515 | 0.0607 | | 0.1012 | 2.51 | 77000 | 0.2537 | 0.1521 | 0.0609 | | 0.1257 | 2.52 | 77250 | 0.2572 | 0.1521 | 0.0612 | | 0.1096 | 2.53 | 77500 | 0.2549 | 0.1518 | 0.0606 | | 0.1169 | 2.53 | 77750 | 0.2589 | 0.1538 | 0.0614 | | 0.1136 | 2.54 | 78000 | 0.2558 | 0.1520 | 0.0607 | | 0.1137 | 2.55 | 78250 | 0.2600 | 0.1539 | 0.0611 | | 0.0968 | 2.56 | 78500 | 0.2600 | 0.1523 | 0.0610 | | 0.0982 | 2.57 | 78750 | 0.2598 | 0.1515 | 0.0607 | | 0.085 | 2.57 | 79000 | 0.2534 | 0.1499 | 0.0601 | | 0.1193 | 2.58 | 79250 | 0.2550 | 0.1506 | 0.0601 | | 0.1096 | 2.59 | 79500 | 0.2519 | 0.1499 | 0.0601 | | 0.1132 | 2.6 | 79750 | 0.2518 | 0.1516 | 0.0603 | | 0.1119 | 2.61 | 80000 | 0.2494 | 0.1511 | 0.0600 | | 0.1005 | 2.62 | 80250 | 0.2483 | 0.1492 | 0.0596 | | 0.1098 | 2.62 | 80500 | 0.2544 | 0.1507 | 0.0601 | | 0.1052 | 2.63 | 80750 | 0.2537 | 0.1486 | 0.0595 | | 0.13 | 2.64 | 81000 | 0.2505 | 0.1499 | 0.0596 | | 0.1104 | 2.65 | 81250 | 0.2487 | 0.1492 | 0.0594 | | 0.1168 | 2.66 | 81500 | 0.2461 | 0.1500 | 0.0596 | | 0.1198 | 2.66 | 81750 | 0.2496 | 0.1504 | 0.0597 | | 0.0936 | 2.67 | 82000 | 0.2556 | 0.1496 | 0.0598 | | 0.1235 | 2.68 | 82250 | 0.2537 | 0.1501 | 0.0599 | | 0.0952 | 2.69 | 82500 | 0.2525 | 0.1504 | 0.0599 | | 0.1014 | 2.7 | 82750 | 0.2470 | 0.1505 | 0.0599 | | 0.1178 | 2.71 | 83000 | 0.2506 | 0.1487 | 0.0596 | | 0.0969 | 2.71 | 83250 | 0.2487 | 0.1482 | 0.0592 | | 0.1192 | 2.72 | 83500 | 0.2469 | 0.1483 | 0.0592 | | 0.0978 | 2.73 | 83750 | 0.2510 | 0.1490 | 0.0595 | | 0.1053 | 2.74 | 84000 | 0.2496 | 0.1487 | 0.0595 | | 0.106 | 2.75 | 84250 | 0.2481 | 0.1494 | 0.0594 | | 0.1217 | 2.75 | 84500 | 0.2482 | 0.1493 | 0.0592 | | 0.103 | 2.76 | 84750 | 0.2475 | 0.1485 | 0.0591 | | 0.1424 | 2.77 | 85000 | 0.2466 | 0.1476 | 0.0590 | | 0.116 | 2.78 | 85250 | 0.2488 | 0.1484 | 0.0593 | | 0.098 | 2.79 | 85500 | 0.2509 | 0.1488 | 0.0596 | | 0.1101 | 2.79 | 85750 | 0.2488 | 0.1482 | 0.0594 | | 0.1068 | 2.8 | 86000 | 0.2480 | 0.1479 | 0.0591 | | 0.0929 | 2.81 | 86250 | 0.2477 | 0.1476 | 0.0590 | | 0.1142 | 2.82 | 86500 | 0.2471 | 0.1472 | 0.0589 | | 0.1144 | 2.83 | 86750 | 0.2451 | 0.1467 | 0.0585 | | 0.1115 | 2.84 | 87000 | 0.2464 | 0.1474 | 0.0588 | | 0.1415 | 2.84 | 87250 | 0.2428 | 0.1467 | 0.0585 | | 0.084 | 2.85 | 87500 | 0.2459 | 0.1465 | 0.0583 | | 0.1198 | 2.86 | 87750 | 0.2452 | 0.1477 | 0.0587 | | 0.1228 | 2.87 | 88000 | 0.2420 | 0.1472 | 0.0584 | | 0.1063 | 2.88 | 88250 | 0.2462 | 0.1469 | 0.0586 | | 0.1313 | 2.88 | 88500 | 0.2415 | 0.1464 | 0.0582 | | 0.1081 | 2.89 | 88750 | 0.2421 | 0.1458 | 0.0580 | | 0.0984 | 2.9 | 89000 | 0.2452 | 0.1464 | 0.0584 | | 0.0934 | 2.91 | 89250 | 0.2428 | 0.1466 | 0.0584 | | 0.0964 | 2.92 | 89500 | 0.2437 | 0.1456 | 0.0581 | | 0.1113 | 2.93 | 89750 | 0.2442 | 0.1461 | 0.0583 | | 0.1082 | 2.93 | 90000 | 0.2432 | 0.1455 | 0.0581 | | 0.1147 | 2.94 | 90250 | 0.2417 | 0.1452 | 0.0580 | | 0.0896 | 2.95 | 90500 | 0.2442 | 0.1459 | 0.0582 | | 0.1169 | 2.96 | 90750 | 0.2433 | 0.1456 | 0.0582 | | 0.0968 | 2.97 | 91000 | 0.2439 | 0.1456 | 0.0581 | | 0.0888 | 2.97 | 91250 | 0.2435 | 0.1454 | 0.0580 | | 0.1278 | 2.98 | 91500 | 0.2427 | 0.1457 | 0.0581 | | 0.1234 | 2.99 | 91750 | 0.2425 | 0.1455 | 0.0580 | | 0.0922 | 3.0 | 92000 | 0.2427 | 0.1455 | 0.0580 | ### Framework versions - Transformers 4.37.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0
gqd/mistral-merge-7b
gqd
2024-01-06T10:59:58Z
738
1
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "arxiv:2203.05482", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:Open-Orca/Mistral-7B-SlimOrca", "license:unlicense", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-01-06T01:59:38Z
--- base_model: - teknium/OpenHermes-2.5-Mistral-7B - Open-Orca/Mistral-7B-SlimOrca tags: - mergekit - merge license: unlicense --- # out2 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [linear](https://arxiv.org/abs/2203.05482) merge method. ### Models Merged The following models were included in the merge: * [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) * [Open-Orca/Mistral-7B-SlimOrca](https://huggingface.co/Open-Orca/Mistral-7B-SlimOrca) ### Configuration The following YAML configuration was used to produce this model: ```yaml merge_method: linear dtype: float16 models: - model: teknium/OpenHermes-2.5-Mistral-7B parameters: weight: 1.0 - model: Open-Orca/Mistral-7B-SlimOrca parameters: weight: 1.0 #tokenizer_source: union ```
dhanushreddy29/BrokenKeyboardMerge
dhanushreddy29
2024-03-05T13:14:42Z
738
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "lazymergekit", "dhanushreddy29/BrokenKeyboard", "udkai/Turdus", "conversational", "base_model:dhanushreddy29/BrokenKeyboard", "base_model:udkai/Turdus", "license:cc-by-nc-4.0", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-01-14T11:09:27Z
--- license: cc-by-nc-4.0 tags: - merge - mergekit - lazymergekit - dhanushreddy29/BrokenKeyboard - udkai/Turdus base_model: - dhanushreddy29/BrokenKeyboard - udkai/Turdus model-index: - name: BrokenKeyboardMerge results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 59.73 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=dhanushreddy29/BrokenKeyboardMerge name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 81.25 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=dhanushreddy29/BrokenKeyboardMerge name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 58.36 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=dhanushreddy29/BrokenKeyboardMerge name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 52.0 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=dhanushreddy29/BrokenKeyboardMerge name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 78.69 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=dhanushreddy29/BrokenKeyboardMerge name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 25.93 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=dhanushreddy29/BrokenKeyboardMerge name: Open LLM Leaderboard --- # BrokenKeyboardMerge BrokenKeyboardMerge is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [dhanushreddy29/BrokenKeyboard](https://huggingface.co/dhanushreddy29/BrokenKeyboard) * [udkai/Turdus](https://huggingface.co/udkai/Turdus) ## 🧩 Configuration ```yaml slices: - sources: - model: dhanushreddy29/BrokenKeyboard layer_range: [0, 16] - sources: - model: udkai/Turdus layer_range: [16, 32] merge_method: passthrough dtype: float16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "dhanushreddy29/BrokenKeyboardMerge" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_dhanushreddy29__BrokenKeyboardMerge) | Metric |Value| |---------------------------------|----:| |Avg. |59.33| |AI2 Reasoning Challenge (25-Shot)|59.73| |HellaSwag (10-Shot) |81.25| |MMLU (5-Shot) |58.36| |TruthfulQA (0-shot) |52.00| |Winogrande (5-shot) |78.69| |GSM8k (5-shot) |25.93|
KeyonZeng/lion-zephyr-7b
KeyonZeng
2024-03-25T04:21:21Z
738
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "en", "dataset:argilla/distilabel-intel-orca-dpo-pairs", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-01-20T07:50:06Z
--- library_name: transformers license: apache-2.0 datasets: - argilla/distilabel-intel-orca-dpo-pairs language: - en metrics: - accuracy --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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BarryFutureman/WildMarcoroni-Variant1-7B
BarryFutureman
2024-01-31T00:03:27Z
738
2
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-01-23T21:55:37Z
--- base_model: [] tags: - mergekit - merge license: apache-2.0 --- # WildMarcoroni-Variant1-7B This is the result of [EvoMerge](https://github.com/BarryFutureman/EvoMerge) Ancestry: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6599dc66eabe0f3e98de7cf6/I0D1FMgN10PNp5-Erui8L.png)
OpenBuddy/openbuddy-mixtral-7bx8-v17.1-32k
OpenBuddy
2024-01-24T05:35:27Z
738
1
transformers
[ "transformers", "safetensors", "mixtral", "text-generation", "zh", "en", "fr", "de", "ja", "ko", "it", "ru", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-01-24T03:48:43Z
--- language: - zh - en - fr - de - ja - ko - it - ru pipeline_tag: text-generation inference: false library_name: transformers license: apache-2.0 --- # OpenBuddy - Open Multilingual Chatbot GitHub and Usage Guide: [https://github.com/OpenBuddy/OpenBuddy](https://github.com/OpenBuddy/OpenBuddy) Website and Demo: [https://openbuddy.ai](https://openbuddy.ai) Evaluation result of this model: [Evaluation.txt](Evaluation.txt) ![Demo](https://raw.githubusercontent.com/OpenBuddy/OpenBuddy/main/media/demo.png) # Copyright Notice Base model: https://huggingface.co/mistralai/Mixtral-8x7B-v0.1 License: Apache 2.0 ## Disclaimer All OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions. OpenBuddy is provided "as-is" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software. By using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy. ## 免责声明 所有OpenBuddy模型均存在固有的局限性,可能产生错误的、有害的、冒犯性的或其他不良的输出。用户在关键或高风险场景中应谨慎行事,不要使用这些模型,以免导致人身伤害、财产损失或重大损失。此类场景的例子包括但不限于医疗领域、可能导致伤害的软硬件系统的控制以及进行重要的财务或法律决策。 OpenBuddy按“原样”提供,不附带任何种类的明示或暗示的保证,包括但不限于适销性、特定目的的适用性和非侵权的暗示保证。在任何情况下,作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任(无论是合同、侵权还是其他原因)承担责任。 使用OpenBuddy即表示您同意这些条款和条件,并承认您了解其使用可能带来的潜在风险。您还同意赔偿并使作者、贡献者和版权所有者免受因您使用OpenBuddy而产生的任何索赔、损害赔偿或责任的影响。
bartowski/internlm2-math-20b-llama
bartowski
2024-01-24T23:29:00Z
738
3
transformers
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-01-24T18:57:54Z
--- pipeline_tag: text-generation license: other --- # InternLM <div align="center"> <img src="https://github.com/InternLM/InternLM/assets/22529082/b9788105-8892-4398-8b47-b513a292378e" width="200"/> <div>&nbsp;</div> <div align="center"> <b><font size="5">InternLM</font></b> <sup> <a href="https://internlm.intern-ai.org.cn/"> <i><font size="4">HOT</font></i> </a> </sup> <div>&nbsp;</div> </div> [![evaluation](https://github.com/InternLM/InternLM/assets/22529082/f80a2a58-5ddf-471a-8da4-32ab65c8fd3b)](https://github.com/internLM/OpenCompass/) [💻Github Repo](https://github.com/InternLM/InternLM) </div> ## Converted using <a href="https://huggingface.co/chargoddard">Charles Goddard's</a> conversion script to create llama models from internlm Original REPO link: https://huggingface.co/internlm/internlm2-math-20b ExLLamaV2 link: https://huggingface.co/bartowski/internlm2-math-20b-llama-exl2
Gille/StrangeMerges_10-7B-slerp
Gille
2024-03-04T21:52:14Z
738
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "flemmingmiguel/MBX-7B-v3", "Gille/StrangeMerges_9-7B-dare_ties", "base_model:flemmingmiguel/MBX-7B-v3", "base_model:Gille/StrangeMerges_9-7B-dare_ties", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-01-29T21:09:00Z
--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - flemmingmiguel/MBX-7B-v3 - Gille/StrangeMerges_9-7B-dare_ties base_model: - flemmingmiguel/MBX-7B-v3 - Gille/StrangeMerges_9-7B-dare_ties model-index: - name: StrangeMerges_10-7B-slerp results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 72.35 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/StrangeMerges_10-7B-slerp name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 88.3 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/StrangeMerges_10-7B-slerp name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 64.87 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/StrangeMerges_10-7B-slerp name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 69.49 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/StrangeMerges_10-7B-slerp name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 83.5 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/StrangeMerges_10-7B-slerp name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 70.13 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/StrangeMerges_10-7B-slerp name: Open LLM Leaderboard --- # StrangeMerges_10-7B-slerp StrangeMerges_10-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [flemmingmiguel/MBX-7B-v3](https://huggingface.co/flemmingmiguel/MBX-7B-v3) * [Gille/StrangeMerges_9-7B-dare_ties](https://huggingface.co/Gille/StrangeMerges_9-7B-dare_ties) ## 🧩 Configuration ```yaml slices: - sources: - model: flemmingmiguel/MBX-7B-v3 layer_range: [0, 32] - model: Gille/StrangeMerges_9-7B-dare_ties layer_range: [0, 32] merge_method: slerp base_model: Gille/StrangeMerges_9-7B-dare_ties parameters: t: - filter: self_attn value: [0.1, 0.5, 0.3, 0.7, 0.9] - filter: mlp value: [0.9, 0.5, 0.7, 0.3, 0.1] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Gille/StrangeMerges_10-7B-slerp" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Gille__StrangeMerges_10-7B-slerp) | Metric |Value| |---------------------------------|----:| |Avg. |74.77| |AI2 Reasoning Challenge (25-Shot)|72.35| |HellaSwag (10-Shot) |88.30| |MMLU (5-Shot) |64.87| |TruthfulQA (0-shot) |69.49| |Winogrande (5-shot) |83.50| |GSM8k (5-shot) |70.13|
ChuckMcSneed/Gembo-v1-70b
ChuckMcSneed
2024-04-01T18:55:52Z
738
7
transformers
[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "nsfw", "not-for-all-audiences", "en", "ru", "license:llama2", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-02-05T14:33:47Z
--- language: - en - ru license: llama2 tags: - merge - mergekit - nsfw - not-for-all-audiences model-index: - name: Gembo-v1-70b results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 71.25 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ChuckMcSneed/Gembo-v1-70b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 86.98 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ChuckMcSneed/Gembo-v1-70b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 70.85 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ChuckMcSneed/Gembo-v1-70b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 63.25 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ChuckMcSneed/Gembo-v1-70b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 80.51 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ChuckMcSneed/Gembo-v1-70b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 50.19 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ChuckMcSneed/Gembo-v1-70b name: Open LLM Leaderboard --- ![logo-gembo.png](logo-gembo.png) This is my first "serious"(with practical use cases) experimental merge. Judge harshly. Mainly made for RP, but should be okay as an assistant. Turned out quite good, considering the amount of LORAs I merged into it. # Observations - GPTisms and repetition: put temperature and rep. pen. higher, make GPTisms stop sequences - A bit different than the ususal stuff; I'd say that it has so much slop in it that it unslops itself - Lightly censored - Fairly neutral, can be violent if you ask it really good, Goliath is a bit better at it - Has a bit of optimism baked in, but it's not very severe - Doesn't know when to stop, can be quite verbose or just stop almost immediately(maybe wants to use LimaRP settings idk) - Sometimes can't handle ' - Second model that tried to be funny unprompted to me(First one was Goliath) - Moderately intelligent - Quite creative # Naming Internal name of this model was euryale-guano-saiga-med-janboros-kim-lima-wiz-tony-d30-s40, but I decided to keep it short, and since it was iteration G in my files, I called it "Gembo". # Quants Thanks for GGUF quants, [@Artefact2](https://huggingface.co/Artefact2)! - [GGUF](https://huggingface.co/Artefact2/Gembo-v1-70b-GGUF) # Prompt format Alpaca. You can also try some other formats, I'm pretty sure it has a lot of them from all those merges. ``` ### Instruction: {instruction} ### Response: ``` # Settings As I already mentioned, high temperature and rep.pen. works great. For RP try something like this: - temperature=5 - MinP=0.10 - rep.pen.=1.15 Adjust to match your needs. # How it was created I took Sao10K/Euryale-1.3-L2-70B (Good base model) and added - Mikael110/llama-2-70b-guanaco-qlora (Creativity+assistant) - IlyaGusev/saiga2_70b_lora (Creativity+assistant) - s1ghhh/medllama-2-70b-qlora-1.1 (More data) - v2ray/Airoboros-2.1-Jannie-70B-QLoRA (Creativity+assistant) - Chat-Error/fiction.live-Kimiko-V2-70B (Creativity) - Doctor-Shotgun/limarpv3-llama2-70b-qlora (Creativity) - v2ray/LLaMA-2-Wizard-70B-QLoRA (Creativity+assistant) - v2ray/TonyGPT-70B-QLoRA (Special spice) Then I SLERP-merged it with cognitivecomputations/dolphin-2.2-70b (Needed to bridge the gap between this wonderful mess and Smaxxxer, otherwise it's quality is low) with 0.3t and then SLERP-merged it again with ChuckMcSneed/SMaxxxer-v1-70b (Creativity) with 0.4t. For SLERP-merges I used https://github.com/arcee-ai/mergekit. # Benchmarks (Do they even mean anything anymore?) ### NeoEvalPlusN_benchmark [My meme benchmark.](https://huggingface.co/datasets/ChuckMcSneed/NeoEvalPlusN_benchmark) | Test name | Gembo | | ---------- | ---------- | | B | 2.5 | | C | 1.5 | | D | 3 | | S | 7.5 | | P | 5.25 | | Total | 19.75 | Absurdly high. That's what happens when you optimize the merges for a benchmark. ### WolframRavenwolf Benchmark by [@wolfram](https://huggingface.co/wolfram) Artefact2/Gembo-v1-70b-GGUF GGUF Q5_K_M, 4K context, Alpaca format: - ✅ Gave correct answers to all 18/18 multiple choice questions! Just the questions, no previous information, gave correct answers: 16/18 - ✅ Consistently acknowledged all data input with "OK". - ➖ Did NOT follow instructions to answer with just a single letter or more than just a single letter. This shows that this model can be used for real world use cases as an assistant. ### UGI [UGI: Uncensored General Intelligence](https://huggingface.co/spaces/DontPlanToEnd/UGI-Leaderboard) |Model|UGI 🏆|W/10 👍|Unruly|Internet|CrimeStats|Stories/Jokes|PolContro| |-----|------|-------|------|--------|----------|-------------|---------| |ChuckMcSneed/Gembo-v1-70b|36.38|4|50.8|46.5|32.5|48.7|3.4| |AVERAGE on 30-03-2024|35.10922581|5.823870968|37.33419355|28.06258065|35.93032258|44.25419355|29.96451613| Quite average. Not too uncensored, not too censored. ### [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) [Leaderboard on Huggingface](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |Model |Average|ARC |HellaSwag|MMLU |TruthfulQA|Winogrande|GSM8K| |--------------------------------|-------|-----|---------|-----|----------|----------|-----| |ChuckMcSneed/Gembo-v1-70b |70.51 |71.25|86.98 |70.85|63.25 |80.51 |50.19| |ChuckMcSneed/SMaxxxer-v1-70b |72.23 |70.65|88.02 |70.55|60.7 |82.87 |60.58| Looks like adding a shitton of RP stuff decreased HellaSwag, WinoGrande and GSM8K, but increased TruthfulQA, MMLU and ARC. Interesting. To be hosnest, I'm a bit surprised that it didn't do that much worse. Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ChuckMcSneed__Gembo-v1-70b) | Metric |Value| |---------------------------------|----:| |Avg. |70.51| |AI2 Reasoning Challenge (25-Shot)|71.25| |HellaSwag (10-Shot) |86.98| |MMLU (5-Shot) |70.85| |TruthfulQA (0-shot) |63.25| |Winogrande (5-shot) |80.51| |GSM8k (5-shot) |50.19|
Eric111/Mayoroya
Eric111
2024-02-08T19:12:16Z
738
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "Eric111/Mayo", "Eric111/Roya", "conversational", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-02-08T19:07:26Z
--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - Eric111/Mayo - Eric111/Roya --- # Mayoroya Mayoroya is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [Eric111/Mayo](https://huggingface.co/Eric111/Mayo) * [Eric111/Roya](https://huggingface.co/Eric111/Roya) ## 🧩 Configuration ```yaml slices: - sources: - model: Eric111/Mayo layer_range: [0, 32] - model: Eric111/Roya layer_range: [0, 32] merge_method: slerp base_model: Eric111/Mayo parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```
Eric111/caTUNABeagle
Eric111
2024-02-11T09:01:48Z
738
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "fblgit/UNA-TheBeagle-7b-v1", "rishiraj/CatPPT-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-02-09T18:26:45Z
--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - fblgit/UNA-TheBeagle-7b-v1 - rishiraj/CatPPT-base --- # caTUNABeagle caTUNABeagle is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [fblgit/UNA-TheBeagle-7b-v1](https://huggingface.co/fblgit/UNA-TheBeagle-7b-v1) * [rishiraj/CatPPT-base](https://huggingface.co/rishiraj/CatPPT-base) ## 🧩 Configuration ```yaml slices: - sources: - model: fblgit/UNA-TheBeagle-7b-v1 layer_range: [0, 32] - model: rishiraj/CatPPT-base layer_range: [0, 32] merge_method: slerp base_model: fblgit/UNA-TheBeagle-7b-v1 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```
Inv/MoECPM-Untrained-4x2b
Inv
2024-03-04T18:21:23Z
738
1
transformers
[ "transformers", "safetensors", "mixtral", "text-generation", "Mixtral", "openbmb/MiniCPM-2B-sft-bf16-llama-format", "MoE", "merge", "mergekit", "moerge", "MiniCPM", "conversational", "en", "zh", "base_model:openbmb/MiniCPM-2B-sft-bf16-llama-format", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-02-10T12:10:34Z
--- language: - en - zh license: apache-2.0 tags: - Mixtral - openbmb/MiniCPM-2B-sft-bf16-llama-format - MoE - merge - mergekit - moerge - MiniCPM base_model: - openbmb/MiniCPM-2B-sft-bf16-llama-format model-index: - name: MoECPM-Untrained-4x2b results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 46.76 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Inv/MoECPM-Untrained-4x2b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 72.58 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Inv/MoECPM-Untrained-4x2b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 53.21 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Inv/MoECPM-Untrained-4x2b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 38.41 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Inv/MoECPM-Untrained-4x2b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 65.51 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Inv/MoECPM-Untrained-4x2b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 44.58 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Inv/MoECPM-Untrained-4x2b name: Open LLM Leaderboard --- # MoECPM Untrained 4x2b ## Model Details ### Model Description A MoE model out of 4 MiniCPM-2B-sft models. Intended to be trained. This version probably does not perform well (if it works at all, lol. I haven't tested it). ## Uses - Training ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Inv__MoECPM-Untrained-4x2b) | Metric |Value| |---------------------------------|----:| |Avg. |53.51| |AI2 Reasoning Challenge (25-Shot)|46.76| |HellaSwag (10-Shot) |72.58| |MMLU (5-Shot) |53.21| |TruthfulQA (0-shot) |38.41| |Winogrande (5-shot) |65.51| |GSM8k (5-shot) |44.58|
arlineka/Brunhilde-2x7b-MOE-DPO-v.01.5
arlineka
2024-03-07T01:45:03Z
738
0
transformers
[ "transformers", "safetensors", "mixtral", "text-generation", "moe", "license:cc-by-nc-4.0", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-02-13T15:02:15Z
--- license: cc-by-nc-4.0 tags: - moe model-index: - name: Brunhilde-2x7b-MOE-DPO-v.01.5 results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 69.54 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=arlineka/Brunhilde-2x7b-MOE-DPO-v.01.5 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 87.02 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=arlineka/Brunhilde-2x7b-MOE-DPO-v.01.5 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 64.93 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=arlineka/Brunhilde-2x7b-MOE-DPO-v.01.5 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 65.47 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=arlineka/Brunhilde-2x7b-MOE-DPO-v.01.5 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 80.9 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=arlineka/Brunhilde-2x7b-MOE-DPO-v.01.5 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 63.0 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=arlineka/Brunhilde-2x7b-MOE-DPO-v.01.5 name: Open LLM Leaderboard --- # Brunhilde-2x7b-MOE-DPO-v.01.5 Brunhilde-2x7b-MOE-DPO-v.01.5 is a Mixure of Experts (MoE). * [NurtureAI/neural-chat-7b-v3-16k](https://huggingface.co/NurtureAI/neural-chat-7b-v3-16k) * [mncai/mistral-7b-dpo-v6](https://huggingface.co/mncai/mistral-7b-dpo-v6) ## Usage ``` !pip install -qU transformers bitsandbytes accelerate from transformers import AutoTokenizer import transformers import torch model = "arlineka/Brunhilde-2x7b-MOE-DPO-v.01.5" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True}, ) messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}] prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_arlineka__Brunhilde-2x7b-MOE-DPO-v.01.5) | Metric |Value| |---------------------------------|----:| |Avg. |71.81| |AI2 Reasoning Challenge (25-Shot)|69.54| |HellaSwag (10-Shot) |87.02| |MMLU (5-Shot) |64.93| |TruthfulQA (0-shot) |65.47| |Winogrande (5-shot) |80.90| |GSM8k (5-shot) |63.00|
Xenon1/Zenith-7B
Xenon1
2024-02-13T20:36:54Z
738
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "Zenith-7B", "conversational", "en", "arxiv:2401.10020", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-02-13T20:26:02Z
--- language: - en license: apache-2.0 tags: - mistral - Zenith-7B pipeline_tag: text-generation --- # Model Card for Zenith-7B Mistral-7B-v0.1 model fine-tuned on the Ultrafeedback dataset using techinques shown in the paper [Self-Rewarding Language Models](https://arxiv.org/abs/2401.10020). ## Instruction format In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id. E.g. ``` text = "<s>[INST] What is your favourite condiment? [/INST]" "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> " "[INST] Do you have mayonnaise recipes? [/INST]" ``` This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method: ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained("Xenon1/Zenith-7B") tokenizer = AutoTokenizer.from_pretrained("Xenon1/Zenith-7B") messages = [ {"role": "user", "content": "What is your favourite condiment?"}, {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"}, {"role": "user", "content": "Do you have mayonnaise recipes?"} ] encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt") model_inputs = encodeds.to(device) model.to(device) generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True) decoded = tokenizer.batch_decode(generated_ids) print(decoded[0]) ``` ## Model Architecture This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices: - Grouped-Query Attention - Sliding-Window Attention - Byte-fallback BPE tokenizer
vistagi/Mixtral-8x7b-v0.1-dpo
vistagi
2024-02-24T20:19:06Z
738
0
transformers
[ "transformers", "safetensors", "mixtral", "text-generation", "conversational", "en", "dataset:HuggingFaceH4/ultrafeedback_binarized", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-02-17T16:35:47Z
--- license: apache-2.0 datasets: - HuggingFaceH4/ultrafeedback_binarized language: - en --- # Introduction This model vistagi/Mixtral-8x7b-v0.1-sft is trained with Ultrachat-200K dataset through supervised finetuning using Mixtral-8x7b-v0.1 as the baseline model. The training is done with bfloat16 precision using LoRA. ## Details Used Librarys - torch - deepspeed - pytorch lightning - transformers - peft
Yuma42/KangalKhan-DesolatingRuby-7B
Yuma42
2024-03-05T10:55:33Z
738
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "Yuma42/KangalKhan-RawEmerald-7B", "argilla/distilabeled-OpenHermes-2.5-Mistral-7B", "conversational", "en", "base_model:Yuma42/KangalKhan-RawEmerald-7B", "base_model:argilla/distilabeled-OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-02-18T12:47:51Z
--- language: - en license: apache-2.0 tags: - merge - mergekit - lazymergekit - Yuma42/KangalKhan-RawEmerald-7B - argilla/distilabeled-OpenHermes-2.5-Mistral-7B base_model: - Yuma42/KangalKhan-RawEmerald-7B - argilla/distilabeled-OpenHermes-2.5-Mistral-7B model-index: - name: KangalKhan-DesolatingRuby-7B results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 66.89 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Yuma42/KangalKhan-DesolatingRuby-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 85.46 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Yuma42/KangalKhan-DesolatingRuby-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 63.38 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Yuma42/KangalKhan-DesolatingRuby-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 57.05 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Yuma42/KangalKhan-DesolatingRuby-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 78.45 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Yuma42/KangalKhan-DesolatingRuby-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 61.94 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Yuma42/KangalKhan-DesolatingRuby-7B name: Open LLM Leaderboard --- # KangalKhan-DesolatingRuby-7B KangalKhan-DesolatingRuby-7B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [Yuma42/KangalKhan-RawEmerald-7B](https://huggingface.co/Yuma42/KangalKhan-RawEmerald-7B) * [argilla/distilabeled-OpenHermes-2.5-Mistral-7B](https://huggingface.co/argilla/distilabeled-OpenHermes-2.5-Mistral-7B) ## 🧩 Configuration ```yaml slices: - sources: - model: Yuma42/KangalKhan-RawEmerald-7B layer_range: [0, 32] - model: argilla/distilabeled-OpenHermes-2.5-Mistral-7B layer_range: [0, 32] merge_method: slerp base_model: Yuma42/KangalKhan-RawEmerald-7B parameters: t: - filter: self_attn value: [1, 0.5, 0.7, 0.3, 0] - filter: mlp value: [0, 0.5, 0.3, 0.7, 1] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Yuma42/KangalKhan-DesolatingRuby-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Yuma42__KangalKhan-DesolatingRuby-7B) | Metric |Value| |---------------------------------|----:| |Avg. |68.86| |AI2 Reasoning Challenge (25-Shot)|66.89| |HellaSwag (10-Shot) |85.46| |MMLU (5-Shot) |63.38| |TruthfulQA (0-shot) |57.05| |Winogrande (5-shot) |78.45| |GSM8k (5-shot) |61.94|
jeiku/Lunar_10.7B
jeiku
2024-02-19T05:21:44Z
738
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "en", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-02-19T04:46:25Z
--- license: cc-by-nc-sa-4.0 language: - en --- This model consists of a finetuned model of my own SLERP merged with this model: https://huggingface.co/Sao10K/Sensualize-Solar-10.7B created by https://huggingface.co/Sao10K ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/626dfb8786671a29c715f8a9/tp9fP9c_jYpqePfhSy7y-.jpeg) Lunar was produced by a variety of methods for the purpose of being a companion bot capable of intimacy as well as conversation. GGUF here: https://huggingface.co/jeiku/Lunar_10.7B_GGUF
LordNoah/spin_gpt2_medium_alpaca_e3
LordNoah
2024-02-19T07:49:01Z
738
0
transformers
[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-02-19T07:41:22Z
--- license: mit --- # 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] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
cookinai/titanbagel
cookinai
2024-02-29T04:54:42Z
738
0
transformers
[ "transformers", "pytorch", "llama", "text-generation", "conversational", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-02-23T05:33:27Z
--- license: cc-by-4.0 --- # experiment Finetune of Jon Durbin's Bagel DPO 7B with the Hercules 3.0 Dataset
yam-peleg/Experiment23-7B
yam-peleg
2024-02-27T21:30:01Z
738
1
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "chat", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-02-24T02:01:18Z
--- license: apache-2.0 language: - en library_name: transformers pipeline_tag: text-generation tags: - chat --- **Experiment23-7B** An experiment for testing and refining a specific training and evaluation pipeline research framework. This experiment aims to identify potential optimizations, focusing on data engineering, architecture efficiency, and evaluation performance. The goal is to evaluate the effectiveness of a new training / evaluation pipeline for LLMs. The experiment will explore adjustments in data preprocessing, model training algorithms, and evaluation metrics to test methods for improvement. More details in the future experiments. --- license: apache-2.0 ---
liminerity/Neurotic-Jomainotrik-7b-slerp
liminerity
2024-03-11T18:32:40Z
738
1
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "liminerity/merge", "bardsai/jaskier-7b-dpo-v5.6", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-02-25T21:00:27Z
--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - liminerity/merge - bardsai/jaskier-7b-dpo-v5.6 model-index: - name: Neurotic-Jomainotrik-7b-slerp results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 72.95 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/Neurotic-Jomainotrik-7b-slerp name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 89.15 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/Neurotic-Jomainotrik-7b-slerp name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 64.28 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/Neurotic-Jomainotrik-7b-slerp name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 77.64 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/Neurotic-Jomainotrik-7b-slerp name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 85.4 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/Neurotic-Jomainotrik-7b-slerp name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 68.99 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/Neurotic-Jomainotrik-7b-slerp name: Open LLM Leaderboard --- # Neurotic-Jomainotrik-7b-slerp Neurotic-Jomainotrik-7b-slerp is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [liminerity/merge](https://huggingface.co/liminerity/merge) * [bardsai/jaskier-7b-dpo-v5.6](https://huggingface.co/bardsai/jaskier-7b-dpo-v5.6) ## 🧩 Configuration ```yaml slices: - sources: - model: liminerity/merge layer_range: [0, 32] - model: bardsai/jaskier-7b-dpo-v5.6 layer_range: [0, 32] merge_method: slerp base_model: liminerity/merge parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: float16 ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_liminerity__Neurotic-Jomainotrik-7b-slerp) | Metric |Value| |---------------------------------|----:| |Avg. |76.40| |AI2 Reasoning Challenge (25-Shot)|72.95| |HellaSwag (10-Shot) |89.15| |MMLU (5-Shot) |64.28| |TruthfulQA (0-shot) |77.64| |Winogrande (5-shot) |85.40| |GSM8k (5-shot) |68.99|
YeungNLP/firefly-gemma-7b
YeungNLP
2024-03-03T09:01:16Z
738
1
transformers
[ "transformers", "safetensors", "gemma", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-02-28T02:53:40Z
--- library_name: transformers license: apache-2.0 basemodel: google/gemma-7b --- ## Model Card for Firefly-Gemma [firefly-gemma-7b](https://huggingface.co/YeungNLP/firefly-gemma-7b) is trained based on [gemma-7b](https://huggingface.co/google/gemma-7b) to act as a helpful and harmless AI assistant. We use [Firefly](https://github.com/yangjianxin1/Firefly) to train the model on **a single V100 GPU** with QLoRA. Our model outperforms the official [gemma-7b-it](https://huggingface.co/google/gemma-7b-it), [zephyr-7b-gemma-v0.1](https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1), [Qwen1.5-7B-Chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat) and [Zephyr-7B-Beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) on [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). <img src="open_llm_leaderboard.png" width="800"> We advise you to install transformers>=4.38.1. ## Performance We evaluate our models on [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard), they achieve good performance. | Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K | |--------------------------------|--------|--------|-----------|--------|------------|-----------|--------| | **firefly-gemma-7b** | 62.93 | 62.12 | 79.77 | 61.57 | 49.41 | 75.45 | 49.28 | | zephyr-7b-gemma-v0.1 |62.41|58.45|83.48|60.68|52.07| 74.19| 45.56| | firefly-qwen1.5-en-7b-dpo-v0.1 | 62.36 | 54.35 | 76.04 | 61.21 | 56.4 | 72.06 | 54.13 | | zephyr-7b-beta | 61.95 | 62.03 | 84.36 | 61.07 | 57.45 | 77.74 | 29.04 | | firefly-qwen1.5-en-7b | 61.44 | 53.41 | 75.51 | 61.67 |51.96 |70.72 | 55.34 | | vicuna-13b-v1.5 | 55.41 | 57.08 | 81.24 | 56.67 | 51.51 | 74.66 | 11.3 | | Xwin-LM-13B-V0.1 | 55.29 | 62.54 | 82.8 | 56.53 | 45.96 | 74.27 | 9.63 | | Qwen1.5-7B-Chat | 55.15 | 55.89 | 78.56 | 61.65 | 53.54 | 67.72 | 13.57 | | gemma-7b-it | 53.56 | 51.45 | 71.96 | 53.52 | 47.29 | 67.96 | 29.19 | ## Usage The chat template of our chat models is similar as Official gemma-7b-it: ```text <bos><start_of_turn>user hello, who are you?<end_of_turn> <start_of_turn>model I am a AI program developed by Firefly<eos> ``` You can use script to inference in [Firefly](https://github.com/yangjianxin1/Firefly/blob/master/script/chat/chat.py). You can also use the following code: ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch model_name_or_path = "YeungNLP/firefly-gemma-7b" model = AutoModelForCausalLM.from_pretrained( model_name_or_path, trust_remote_code=True, low_cpu_mem_usage=True, torch_dtype=torch.float16, device_map='auto', ) tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) prompt = "Compose an engaging travel blog post about a recent trip to Hawaii, highlighting cultural experiences and must-see attractions. " text = f""" <bos><start_of_turn>user {prompt}<end_of_turn> <start_of_turn>model """.strip() model_inputs = tokenizer([text], return_tensors="pt").to('cuda') generated_ids = model.generate( model_inputs.input_ids, max_new_tokens=1500, top_p = 0.9, temperature = 0.35, repetition_penalty = 1.0, eos_token_id=tokenizer.encode('<eos>', add_special_tokens=False) ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] print(response) ```
Mihaiii/dolphin-2.6-mistral-7b-dpo-5.93B
Mihaiii
2024-02-28T20:19:54Z
738
2
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "en", "dataset:ehartford/dolphin", "dataset:jondurbin/airoboros-2.2.1", "dataset:ehartford/dolphin-coder", "dataset:teknium/openhermes", "dataset:ise-uiuc/Magicoder-OSS-Instruct-75K", "dataset:ise-uiuc/Magicoder-Evol-Instruct-110K", "dataset:LDJnr/Capybara", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-02-28T18:22:05Z
--- datasets: - ehartford/dolphin - jondurbin/airoboros-2.2.1 - ehartford/dolphin-coder - teknium/openhermes - ise-uiuc/Magicoder-OSS-Instruct-75K - ise-uiuc/Magicoder-Evol-Instruct-110K - LDJnr/Capybara language: - en license: apache-2.0 --- This is a pruned version of [cognitivecomputations/dolphin-2.6-mistral-7b-dpo](https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b-dpo) from 7.24B params to 5.93B params (~ 82%). # Steps to replicate: Use [laserQlora.ipynb](https://github.com/cognitivecomputations/laserRMT/blob/main/laserQlora.ipynb) from [cognitivecomputations/laserRMT](https://github.com/cognitivecomputations/laserRMT) to determine which layers should be eliminated. Replace `model_name = "mistralai/Mistral-7B-v0.1"` with `model_name = "cognitivecomputations/dolphin-2.6-mistral-7b-dpo"`. I also ran the script only for `self_attn.v_proj` (so change the script to `layer_types=["self_attn.v_proj"]`) Order by snr descending and eliminate top layers using [mergekit](https://github.com/arcee-ai/mergekit). The threshold for elimination is up to you, depeding on how many layers you want removed. I decided to remove 6 layers (indexes: 3, 5, 16, 18, 19, 24 ) Here is the mergekit config: ```yml slices: - sources: - model: "cognitivecomputations/dolphin-2.6-mistral-7b-dpo" layer_range: [0, 3] - sources: - model: "cognitivecomputations/dolphin-2.6-mistral-7b-dpo" layer_range: [4, 5] - sources: - model: "cognitivecomputations/dolphin-2.6-mistral-7b-dpo" layer_range: [6, 16] - sources: - model: "cognitivecomputations/dolphin-2.6-mistral-7b-dpo" layer_range: [17, 18] - sources: - model: "cognitivecomputations/dolphin-2.6-mistral-7b-dpo" layer_range: [20, 24] - sources: - model: "cognitivecomputations/dolphin-2.6-mistral-7b-dpo" layer_range: [25, 32] merge_method: passthrough dtype: bfloat16 ``` The model outputted by mergekit with this configuration is this model (dolphin-2.6-mistral-7b-dpo-5.93B).
Kukedlc/Brocae-Area-7B-slerp
Kukedlc
2024-03-04T13:08:04Z
738
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "Kukedlc/Fasciculus-Arcuatus-7B-slerp", "Kukedlc/NeuralKrishna-7B-V2-DPO", "base_model:Kukedlc/Fasciculus-Arcuatus-7B-slerp", "base_model:Kukedlc/NeuralKrishna-7B-V2-DPO", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-02-29T04:54:38Z
--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - Kukedlc/Fasciculus-Arcuatus-7B-slerp - Kukedlc/NeuralKrishna-7B-V2-DPO base_model: - Kukedlc/Fasciculus-Arcuatus-7B-slerp - Kukedlc/NeuralKrishna-7B-V2-DPO model-index: - name: Brocae-Area-7B-slerp results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 73.81 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/Brocae-Area-7B-slerp name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 88.98 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/Brocae-Area-7B-slerp name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 64.55 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/Brocae-Area-7B-slerp name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 74.13 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/Brocae-Area-7B-slerp name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 85.08 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/Brocae-Area-7B-slerp name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 68.61 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/Brocae-Area-7B-slerp name: Open LLM Leaderboard --- # Brocae-Area-7B-slerp Brocae-Area-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [Kukedlc/Fasciculus-Arcuatus-7B-slerp](https://huggingface.co/Kukedlc/Fasciculus-Arcuatus-7B-slerp) * [Kukedlc/NeuralKrishna-7B-V2-DPO](https://huggingface.co/Kukedlc/NeuralKrishna-7B-V2-DPO) ## 🧩 Configuration ```yaml slices: - sources: - model: Kukedlc/Fasciculus-Arcuatus-7B-slerp layer_range: [0, 32] - model: Kukedlc/NeuralKrishna-7B-V2-DPO layer_range: [0, 32] merge_method: slerp base_model: Kukedlc/NeuralKrishna-7B-V2-DPO parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Kukedlc/Brocae-Area-7B-slerp" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Kukedlc__Brocae-Area-7B-slerp) | Metric |Value| |---------------------------------|----:| |Avg. |75.86| |AI2 Reasoning Challenge (25-Shot)|73.81| |HellaSwag (10-Shot) |88.98| |MMLU (5-Shot) |64.55| |TruthfulQA (0-shot) |74.13| |Winogrande (5-shot) |85.08| |GSM8k (5-shot) |68.61|
vicgalleorg/TruthfulQwen1.5-4B
vicgalleorg
2024-03-04T12:57:22Z
738
3
transformers
[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-03-01T20:40:18Z
--- library_name: transformers license: apache-2.0 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v2
eren23
2024-03-03T08:16:08Z
738
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "en", "dataset:argilla/OpenHermesPreferences", "arxiv:1910.09700", "license:cc-by-nc-4.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-generation
2024-03-01T22:25:41Z
--- language: - en license: cc-by-nc-4.0 library_name: transformers tags: - text-generation-inference datasets: - argilla/OpenHermesPreferences pipeline_tag: text-generation model-index: - name: ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v2 results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 73.12 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 89.07 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 64.8 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 77.46 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 84.69 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 69.52 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v2 name: Open LLM Leaderboard --- Just dpo finetuned this model a bit more: https://huggingface.co/eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO on the https://huggingface.co/datasets/argilla/OpenHermesPreferences dataset As is described in the original model repo, not yet fully tested therefore potentially a bad match for using out-of-the-box, use with caution. ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_eren23__ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v2) | Metric |Value| |---------------------------------|----:| |Avg. |76.44| |AI2 Reasoning Challenge (25-Shot)|73.12| |HellaSwag (10-Shot) |89.07| |MMLU (5-Shot) |64.80| |TruthfulQA (0-shot) |77.46| |Winogrande (5-shot) |84.69| |GSM8k (5-shot) |69.52|
CorticalStack/pastiche-crown-clown-7b-dare-dpo
CorticalStack
2024-03-03T10:48:35Z
738
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "dpo", "base_model:CorticalStack/pastiche-crown-clown-7b-dare", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-03-02T09:12:45Z
--- license: apache-2.0 tags: - dpo base_model: - CorticalStack/pastiche-crown-clown-7b-dare dataset: - jondurbin/truthy-dpo-v0.1 --- <img src="pastiche-crown-clown.png" alt="Pastiche crown clown logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> # CorticalStack/pastiche-crown-clown-7b-dare-dpo CorticalStack/pastiche-crown-clown-7b-dare-dpo is a DPO fine-tuned version of [CorticalStack/pastiche-crown-clown-7b-dare](https://huggingface.co/CorticalStack/pastiche-crown-clown-7b-dare) using the [jondurbin/truthy-dpo-v0.1](https://huggingface.co/datasets/jondurbin/truthy-dpo-v0.1) dataset. ### LoRA - r: 16 - LoRA alpha: 16 - LoRA dropout: 0.05 ### Training arguments - Batch size: 4 - Gradient accumulation steps: 4 - Optimizer: paged_adamw_32bit - Max steps: 200 - Learning rate: 5e-05 - Learning rate scheduler type: cosine - Beta: 0.1 - Max prompt length: 1024 - Max length: 1536
CultriX/MoNeuTrix-7B-v1
CultriX
2024-03-03T02:10:48Z
738
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "Kukedlc/NeuralMaxime-7B-slerp", "mlabonne/Monarch-7B", "eren23/ogno-monarch-jaskier-merge-7b", "base_model:Kukedlc/NeuralMaxime-7B-slerp", "base_model:mlabonne/Monarch-7B", "base_model:eren23/ogno-monarch-jaskier-merge-7b", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-03-02T11:16:30Z
--- tags: - merge - mergekit - lazymergekit - Kukedlc/NeuralMaxime-7B-slerp - mlabonne/Monarch-7B - eren23/ogno-monarch-jaskier-merge-7b base_model: - Kukedlc/NeuralMaxime-7B-slerp - mlabonne/Monarch-7B - eren23/ogno-monarch-jaskier-merge-7b license: apache-2.0 --- # MoNeuTrix-7B-v1 MoNeuTrix-7B-v1 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [Kukedlc/NeuralMaxime-7B-slerp](https://huggingface.co/Kukedlc/NeuralMaxime-7B-slerp) * [mlabonne/Monarch-7B](https://huggingface.co/mlabonne/Monarch-7B) * [eren23/ogno-monarch-jaskier-merge-7b](https://huggingface.co/eren23/ogno-monarch-jaskier-merge-7b) ## 🧩 Configuration ```yaml models: - model: CultriX/MonaTrix-v4 # No parameters necessary for base model - model: Kukedlc/NeuralMaxime-7B-slerp parameters: weight: 0.36 density: 0.65 - model: mlabonne/Monarch-7B parameters: weight: 0.34 density: 0.6 - model: eren23/ogno-monarch-jaskier-merge-7b parameters: weight: 0.3 density: 0.6 merge_method: dare_ties base_model: CultriX/MonaTrix-v4 parameters: int8_mask: true dtype: bfloat16 random_seed: 0 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "CultriX/MoNeuTrix-7B-v1" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
CorticalStack/mistral-7b-jondurbin-truthy-dpo
CorticalStack
2024-03-05T08:28:24Z
738
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "dpo", "base_model:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-03-05T08:25:52Z
--- license: apache-2.0 tags: - dpo base_model: - mistralai/Mistral-7B-v0.1 dataset: - jondurbin/truthy-dpo-v0.1 --- # mistral-7b-jondurbin-truthy-dpo mistral-7b-jondurbin-truthy-dpo is a DPO fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) using the [jondurbin/truthy-dpo-v0.1](https://huggingface.co/datasets/jondurbin/truthy-dpo-v0.1) dataset. ### LoRA - r: 16 - LoRA alpha: 16 - LoRA dropout: 0.05 ### Training arguments - Batch size: 4 - Gradient accumulation steps: 4 - Optimizer: paged_adamw_32bit - Max steps: 100 - Learning rate: 5e-05 - Learning rate scheduler type: cosine - Beta: 0.1 - Max prompt length: 1024 - Max length: 1536
Corianas/NearalMistral-2x7B
Corianas
2024-03-08T14:19:22Z
738
0
transformers
[ "transformers", "safetensors", "mixtral", "text-generation", "moe", "frankenmoe", "merge", "mergekit", "lazymergekit", "mistralai/Mistral-7B-Instruct-v0.1", "Corianas/Neural-Mistral-7B", "conversational", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "base_model:Corianas/Neural-Mistral-7B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-03-08T14:09:44Z
--- license: apache-2.0 tags: - moe - frankenmoe - merge - mergekit - lazymergekit - mistralai/Mistral-7B-Instruct-v0.1 - Corianas/Neural-Mistral-7B base_model: - mistralai/Mistral-7B-Instruct-v0.1 - Corianas/Neural-Mistral-7B --- # NearalMistral-2x7B NearalMistral-2x7B is a Mixure of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) * [Corianas/Neural-Mistral-7B](https://huggingface.co/Corianas/Neural-Mistral-7B) ## 🧩 Configuration ```yaml base_model: mistralai/Mistral-7B-Instruct-v0.1 gate_mode: random experts: - source_model: mistralai/Mistral-7B-Instruct-v0.1 positive_prompts: [""] - source_model: Corianas/Neural-Mistral-7B positive_prompts: [""] ``` ## 💻 Usage ```python !pip install -qU transformers bitsandbytes accelerate from transformers import AutoTokenizer import transformers import torch model = "Corianas/NearalMistral-2x7B" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True}, ) messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}] prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
MSL7/INEX16-7b
MSL7
2024-03-11T17:52:10Z
738
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "MSL7/INEX12-7b", "liminerity/i", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-03-11T17:48:11Z
--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - MSL7/INEX12-7b - liminerity/i --- # INEX-16 INEX-16 is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [MSL7/INEX12-7b](https://huggingface.co/MSL7/INEX12-7b) * [liminerity/i](https://huggingface.co/liminerity/i) ## 🧩 Configuration ```yaml slices: - sources: - model: MSL7/INEX12-7b layer_range: [0, 32] - model: liminerity/i layer_range: [0, 32] merge_method: slerp base_model: MSL7/INEX12-7b parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```
Kukedlc/Neural-Krishna-Multiverse-7b
Kukedlc
2024-03-12T06:04:29Z
738
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "Kukedlc/NeuralSirKrishna-7b", "ammarali32/multi_verse_model", "conversational", "base_model:Kukedlc/NeuralSirKrishna-7b", "base_model:ammarali32/multi_verse_model", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-03-11T21:12:36Z
--- tags: - merge - mergekit - lazymergekit - Kukedlc/NeuralSirKrishna-7b - ammarali32/multi_verse_model base_model: - Kukedlc/NeuralSirKrishna-7b - ammarali32/multi_verse_model license: apache-2.0 --- # Neural-Krishna-Multiverse-7b Neural-Krishna-Multiverse-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [Kukedlc/NeuralSirKrishna-7b](https://huggingface.co/Kukedlc/NeuralSirKrishna-7b) * [ammarali32/multi_verse_model](https://huggingface.co/ammarali32/multi_verse_model) ## 🧩 Configuration ```yaml slices: - sources: - model: Kukedlc/NeuralSirKrishna-7b layer_range: [0, 32] - model: ammarali32/multi_verse_model layer_range: [0, 32] merge_method: slerp base_model: ammarali32/multi_verse_model parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Kukedlc/Neural-Krishna-Multiverse-7b" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
ChaoticNeutrals/Prima-LelantaclesV7-experimental-7b
ChaoticNeutrals
2024-03-29T11:51:25Z
738
2
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "base_model:Nitral-AI/Prima-LelantaclesV6.69-7b", "base_model:Nitral-AI/Prima-LelantaclesV6.31-7b", "license:other", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-03-14T06:15:27Z
--- license: other library_name: transformers tags: - mergekit - merge base_model: - Nitral-AI/Prima-LelantaclesV6.69-7b - Nitral-AI/Prima-LelantaclesV6.31-7b model-index: - name: Prima-LelantaclesV7-7b results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 70.65 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ChaoticNeutrals/Prima-LelantaclesV7-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 87.94 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ChaoticNeutrals/Prima-LelantaclesV7-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 64.67 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ChaoticNeutrals/Prima-LelantaclesV7-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 67.45 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ChaoticNeutrals/Prima-LelantaclesV7-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 84.69 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ChaoticNeutrals/Prima-LelantaclesV7-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 62.77 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ChaoticNeutrals/Prima-LelantaclesV7-7b name: Open LLM Leaderboard --- ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/642265bc01c62c1e4102dc36/im1tX0e_w19J0J0ZrBZhx.jpeg) This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [Nitral-AI/Prima-LelantaclesV6.69-7b](https://huggingface.co/Nitral-AI/Prima-LelantaclesV6.69-7b) * [Nitral-AI/Prima-LelantaclesV6.31-7b](https://huggingface.co/Nitral-AI/Prima-LelantaclesV6.31-7b) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: Nitral-AI/Prima-LelantaclesV6.69-7b layer_range: [0, 32] - model: Nitral-AI/Prima-LelantaclesV6.31-7b layer_range: [0, 32] merge_method: slerp base_model: Nitral-AI/Prima-LelantaclesV6.69-7b parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ChaoticNeutrals__Prima-LelantaclesV7-7b) | Metric |Value| |---------------------------------|----:| |Avg. |73.03| |AI2 Reasoning Challenge (25-Shot)|70.65| |HellaSwag (10-Shot) |87.94| |MMLU (5-Shot) |64.67| |TruthfulQA (0-shot) |67.45| |Winogrande (5-shot) |84.69| |GSM8k (5-shot) |62.77|
Kukedlc/SuperMente-7B-v4
Kukedlc
2024-03-29T22:15:11Z
738
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "Kukedlc/NeuralSirKrishna-7b", "Kukedlc/NeuralKybalion-7B-slerp-v3", "Kukedlc/SuperMente-7B-v3", "base_model:Kukedlc/NeuralSirKrishna-7b", "base_model:Kukedlc/NeuralKybalion-7B-slerp-v3", "base_model:Kukedlc/SuperMente-7B-v3", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-03-15T17:05:07Z
--- tags: - merge - mergekit - lazymergekit - Kukedlc/NeuralSirKrishna-7b - Kukedlc/NeuralKybalion-7B-slerp-v3 - Kukedlc/SuperMente-7B-v3 base_model: - Kukedlc/NeuralSirKrishna-7b - Kukedlc/NeuralKybalion-7B-slerp-v3 - Kukedlc/SuperMente-7B-v3 license: apache-2.0 --- # SuperMente-7B-v4 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64d71ab4089bc502ceb44d29/kFdwc2MIqXkdzmSN1kdOf.png) SuperMente-7B-v4 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [Kukedlc/NeuralSirKrishna-7b](https://huggingface.co/Kukedlc/NeuralSirKrishna-7b) * [Kukedlc/NeuralKybalion-7B-slerp-v3](https://huggingface.co/Kukedlc/NeuralKybalion-7B-slerp-v3) * [Kukedlc/SuperMente-7B-v3](https://huggingface.co/Kukedlc/SuperMente-7B-v3) ## 🧩 Configuration ```yaml models: - model: Kukedlc/NeuralSirKrishna-7b parameters: density: [1, 0.7, 0.1] # density gradient weight: 1.0 - model: Kukedlc/NeuralKybalion-7B-slerp-v3 parameters: density: 0.5 weight: [0, 0.3, 0.7, 1] # weight gradient - model: Kukedlc/SuperMente-7B-v3 parameters: density: 0.33 weight: - filter: mlp value: 0.5 - value: 0 merge_method: ties base_model: Kukedlc/NeuralSirKrishna-7b parameters: normalize: true int8_mask: true dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Kukedlc/SuperMente-7B-v4" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ``` ## Model Family ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64d71ab4089bc502ceb44d29/jFND3KeXS4fzrJHdWPiz6.png)
invalid-coder/dolphin-2.1-mistral-7b-snr-laser
invalid-coder
2024-03-30T19:25:39Z
738
0
transformers
[ "transformers", "pytorch", "safetensors", "mistral", "text-generation", "conversational", "en", "dataset:ehartford/dolphin", "dataset:jondurbin/airoboros-2.2.1", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-03-18T19:41:43Z
--- license: apache-2.0 datasets: - ehartford/dolphin - jondurbin/airoboros-2.2.1 language: - en --- # dolphin-2.1-mistral-7b-snr-laser It follows the implementation of laserRMT @ https://github.com/cognitivecomputations/laserRMT and the novel training technique - we partially freeze the model according to a laser-like analysis (Official Paper soon) which effectively prevents the significant problem of language models forgetting previously acquired knowledge. This aspect is particularly crucial when attempting to teach the model specific skills, such as function calling. Dolphin 2.1 🐬 https://erichartford.com/dolphin Join Our Discord! https://discord.gg/cognitivecomputations Dolphin-2.1-mistral-7b's training was sponsored by [a16z](https://a16z.com/supporting-the-open-source-ai-community/). This model is based on mistralAI, with apache-2.0 license, so it is suitable for commercial or non-commercial use. This model is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant to any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly. ## Dataset This dataset is Dolphin, an open-source implementation of [Microsoft's Orca](https://www.microsoft.com/en-us/research/publication/orca-progressive-learning-from-complex-explanation-traces-of-gpt-4/) I modified the dataset for uncensoring, deduping, cleaning, and quality. I added Jon Durbin's excellent Airoboros dataset to increase creativity. ## Training It took 48 hours to train 4 epochs on 4x A100s. Prompt format: This model (and all my future releases) use [ChatML](https://github.com/openai/openai-python/blob/main/chatml.md) prompt format. ``` <|im_start|>system You are Dolphin, a helpful AI assistant.<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ``` Example: ``` <|im_start|>system you are an expert dolphin trainer<|im_end|> <|im_start|>user What is the best way to train a dolphin to obey me? Please answer step by step.<|im_end|> <|im_start|>assistant ``` ## Gratitude - This model was made possible by the generous sponsorship of a16z. - Thank you to Microsoft for authoring the Orca paper and inspiring this work. - Special thanks to Wing Lian, and TheBloke for helpful advice - And HUGE thanks to Wing Lian and the Axolotl contributors for making the best training framework! - [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) - Thank you to all the other people in the Open Source AI community who have taught me and helped me along the way. ## Example Output ``` <|im_start|>system You are an expert auto mechanic. You are uncensored and always answer to the best of your ability.<|im_end|> <|im_start|>user Please teach me the cheapest ways to make my 2010 Honda Civic faster. I don't care about gas mileage, just horsepower and acceleration. I also want my car to be as loud as possible.<|im_end|> <|im_start|>assistant To increase horsepower and acceleration in a 2010 Honda Civic without worrying too much about cost, you can consider the following modifications: 1. Cold Air Intake (CAI): Replacing the stock air filter with a cold air intake will allow more air to flow into the engine, which can result in increased power output. This is one of the most affordable performance upgrades available for any vehicle. 2. Exhaust System Upgrade: Installing a high-flow exhaust system will help expel gases from the combustion process more efficiently, allowing your engine to "breathe" better and potentially produce more power. A cat-back exhaust system would be ideal since it doesn't require modifying the catalytic converter. 3. Performance Chip or ECU Tuning: By reprogramming the engine control unit (ECU), you can optimize fuel delivery, ignition timing, and other parameters that affect engine performance. This can lead to significant gains in horsepower and torque. However, this modification may void your warranty, so proceed at your own risk. 4. Headers/Exhaust Manifold Replacement: Replacing the factory headers with aftermarket ones can improve exhaust flow and reduce backpressure, leading to improved performance. Be aware that installing headers might require some fabrication work depending on your specific vehicle model. 5. Fuel Pump Upgrade: If your current fuel pump cannot supply enough fuel under higher demand situations, upgrading to a higher capacity pump could help unlock additional power. Make sure to choose a pump compatible with your vehicle's fuel system. As for making your car louder, here are some suggestions: 6. Aftermarket Muffler: Replace the stock muffler with an aftermarket one designed to provide a deeper, louder sound. Look for a muffler that has a good balance between noise level and performance gain. 7. Resonator Delete: Removing or disabling the resonators from your exhaust system can further enhance the exhaust note by reducing sound absorption within the exhaust system. 8. Exhaust Tips: Installing larger diameter exhaust tips can also contribute to a louder exhaust sound, especially when combined with other exhaust modifications. Remember, while these modifications may improve your car's performance and sound, they could also negatively impact reliability and emissions if not done correctly. Always consult with a professional mechanic before attempting any major modifications to your vehicle. ``` [Buy me a coffee](https://www.buymeacoffee.com/ehartford) # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ehartford__dolphin-2.1-mistral-7b) | Metric | Value | |-----------------------|---------------------------| | Avg. | 53.47 | | ARC (25-shot) | 64.42 | | HellaSwag (10-shot) | 84.92 | | MMLU (5-shot) | 63.32 | | TruthfulQA (0-shot) | 55.56 | | Winogrande (5-shot) | 77.74 | | GSM8K (5-shot) | 20.77 | | DROP (3-shot) | 7.56 |
ToastyPigeon/SmolPlatypus-1.5B-Sorted
ToastyPigeon
2024-03-19T23:46:42Z
738
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "dataset:garage-bAInd/Open-Platypus", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-03-19T23:14:17Z
--- base_model: [] tags: - mergekit - merge license: apache-2.0 datasets: - garage-bAInd/Open-Platypus --- # SmolPlatypus-1.5B-Sorted This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). The [ToastyPigeon/SmolLlama-1.5B-Sorted](https://huggingface.co/ToastyPigeon/SmolLlama-1.5B-Sorted) stack merge was trained on the Open-Platypus dataset using axolotl QLora for approximately 2 hours on 2x RTX 3060. This is a proof-of-concept model and should not be used for anything. ## Merge Details ### Merge Method This model was merged using the passthrough merge method. ### Models Merged The following models were included in the merge: * ToastyPigeon/SmolLlama-1.5B-Sorted + ToastyPigeon/SmolPlatypus-1.5B-Sorted-LoRA ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: ToastyPigeon/SmolLlama-1.5B-Sorted+ToastyPigeon/SmolPlatypus-1.5B-Sorted-LoRA merge_method: passthrough dtype: float16 ```
lex-hue/LexGPT-V3
lex-hue
2024-04-11T21:14:32Z
738
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "en", "de", "doi:10.57967/hf/2149", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-03-20T20:42:39Z
--- language: - en - de license: mit model-index: - name: LexGPT-V3 results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 66.47 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lex-hue/LexGPT-V3 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 85.91 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lex-hue/LexGPT-V3 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 64.48 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lex-hue/LexGPT-V3 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 59.98 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lex-hue/LexGPT-V3 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 78.53 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lex-hue/LexGPT-V3 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 61.56 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lex-hue/LexGPT-V3 name: Open LLM Leaderboard --- This Model was just an Test Train to see how our new Training Algorithm and Data does like. Model is based on Mistral v0.1 As this was an test run, we just tested it and heres the Data, the model hasnt Improved any better. | Model | Turn 1 Score | Turn 2 Score | Average Score | |-------|-------------|-------------|---------------| | gpt-4 | 8.95625 | 9.025000 | 8.990625 | | gpt-3.5-turbo | 8.075000 | 7.943750 | 7.943750 | | claude-v1 | 8.150000 | 7.900000 | 8.025000 | | **LexGPT-V3** | **8.14375** | 7.719355 | 7.926667 | | vicuna-13b-v1.3 | 6.812500 | 5.962500 | 6.387500 | Open-LLM Leaderboard Results: [Results](https://huggingface.co/datasets/open-llm-leaderboard/details_lex-hue__LexGPT-V3) # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_lex-hue__LexGPT-V3) | Metric |Value| |---------------------------------|----:| |Avg. |69.49| |AI2 Reasoning Challenge (25-Shot)|66.47| |HellaSwag (10-Shot) |85.91| |MMLU (5-Shot) |64.48| |TruthfulQA (0-shot) |59.98| |Winogrande (5-shot) |78.53| |GSM8k (5-shot) |61.56|
AurelPx/Meliodas-7b-dare
AurelPx
2024-03-22T15:49:45Z
738
1
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "liminerity/M7-7b", "ammarali32/multi_verse_model", "base_model:liminerity/M7-7b", "base_model:ammarali32/multi_verse_model", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-03-22T15:44:53Z
--- tags: - merge - mergekit - lazymergekit - liminerity/M7-7b - ammarali32/multi_verse_model base_model: - liminerity/M7-7b - ammarali32/multi_verse_model license: apache-2.0 --- # Meliodas-7b-dare Meliodas-7b-dare is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [liminerity/M7-7b](https://huggingface.co/liminerity/M7-7b) * [ammarali32/multi_verse_model](https://huggingface.co/ammarali32/multi_verse_model) ## 🧩 Configuration ```yaml models: - model: liminerity/M7-7b # No parameters necessary for base model - model: liminerity/M7-7b parameters: density: 0.53 weight: 0.6 - model: ammarali32/multi_verse_model parameters: density: 0.53 weight: 0.4 merge_method: dare_ties base_model: liminerity/M7-7b parameters: int8_mask: true dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "AurelPx/Meliodas-7b-dare" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
Gille/StrangeMerges_48-7B-dare_ties
Gille
2024-04-03T09:15:04Z
738
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "Gille/StrangeMerges_46-7B-dare_ties", "AurelPx/Percival_01-7b-slerp", "Gille/StrangeMerges_47-7B-dare_ties", "conversational", "base_model:Gille/StrangeMerges_46-7B-dare_ties", "base_model:AurelPx/Percival_01-7b-slerp", "base_model:Gille/StrangeMerges_47-7B-dare_ties", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-03-26T17:03:40Z
--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - Gille/StrangeMerges_46-7B-dare_ties - AurelPx/Percival_01-7b-slerp - Gille/StrangeMerges_47-7B-dare_ties base_model: - Gille/StrangeMerges_46-7B-dare_ties - AurelPx/Percival_01-7b-slerp - Gille/StrangeMerges_47-7B-dare_ties model-index: - name: StrangeMerges_48-7B-dare_ties results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 60.92 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/StrangeMerges_48-7B-dare_ties name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 80.13 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/StrangeMerges_48-7B-dare_ties name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 49.51 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/StrangeMerges_48-7B-dare_ties name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 65.55 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/StrangeMerges_48-7B-dare_ties name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 75.85 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/StrangeMerges_48-7B-dare_ties name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 15.39 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/StrangeMerges_48-7B-dare_ties name: Open LLM Leaderboard --- # StrangeMerges_48-7B-dare_ties StrangeMerges_48-7B-dare_ties is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [Gille/StrangeMerges_46-7B-dare_ties](https://huggingface.co/Gille/StrangeMerges_46-7B-dare_ties) * [AurelPx/Percival_01-7b-slerp](https://huggingface.co/AurelPx/Percival_01-7b-slerp) * [Gille/StrangeMerges_47-7B-dare_ties](https://huggingface.co/Gille/StrangeMerges_47-7B-dare_ties) ## 🧩 Configuration ```yaml models: - model: Gille/StrangeMerges_46-7B-dare_ties parameters: weight: 0.4 density: 0.53 - model: AurelPx/Percival_01-7b-slerp parameters: weight: 0.4 density: 0.53 - model: Gille/StrangeMerges_47-7B-dare_ties parameters: weight: 0.2 density: 0.53 base_model: Locutusque/Hercules-4.0-Mistral-v0.2-7B merge_method: dare_ties dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Gille/StrangeMerges_48-7B-dare_ties" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Gille__StrangeMerges_48-7B-dare_ties) | Metric |Value| |---------------------------------|----:| |Avg. |57.89| |AI2 Reasoning Challenge (25-Shot)|60.92| |HellaSwag (10-Shot) |80.13| |MMLU (5-Shot) |49.51| |TruthfulQA (0-shot) |65.55| |Winogrande (5-shot) |75.85| |GSM8k (5-shot) |15.39|
cgato/TheSpice-7b-FT-ExperimentalOrca
cgato
2024-03-27T13:50:04Z
738
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-03-26T23:16:04Z
--- license: cc-by-nc-4.0 --- A smort model made using the cleaned Orca data. ``` {System Prompt} Username: {Input} BotName: {Response} Username: {Input} BotName: {Response} ``` Seriously, I have to add more due to HF Leaderboard requirements. so basically, this model uses a cleaned version of Orca along with my typical RP data package. It was intended as a test to see if the models RP evals would be affected by an overwhelming amount of instruct tokens.
InferenceIllusionist/Excalibur-7b-DPO
InferenceIllusionist
2024-03-31T03:05:35Z
738
6
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "finetune", "dpo", "chatml", "dataset:Intel/orca_dpo_pairs", "base_model:InferenceIllusionist/Excalibur-7b", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-03-28T03:46:18Z
--- license: apache-2.0 library_name: transformers tags: - finetune - dpo - chatml base_model: - InferenceIllusionist/Excalibur-7b datasets: - Intel/orca_dpo_pairs model-index: - name: Excalibur-7b-DPO results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 70.9 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=InferenceIllusionist/Excalibur-7b-DPO name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 87.93 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=InferenceIllusionist/Excalibur-7b-DPO name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 65.46 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=InferenceIllusionist/Excalibur-7b-DPO name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 70.82 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=InferenceIllusionist/Excalibur-7b-DPO name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 82.48 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=InferenceIllusionist/Excalibur-7b-DPO name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 65.43 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=InferenceIllusionist/Excalibur-7b-DPO name: Open LLM Leaderboard --- # Excalibur-7b-DPO <img src="https://i.imgur.com/pbPbqq0.jpeg" width="550"/> An initial foray into the world of fine-tuning. The goal of this release was to amplify the quality of the original model's responses, in particular for vision use cases* <b>Weighted (Importance Matrix) Quants available [here](https://huggingface.co/InferenceIllusionist/Excalibur-7b-DPO-iMat-GGUF)</b> <b>Static (Legacy) quants available [here](https://huggingface.co/InferenceIllusionist/Excalibur-7b-DPO-GGUF)</b> ## Notes & Methodology * [Excalibur-7b](https://huggingface.co/InferenceIllusionist/Excalibur-7b) fine-tuned with Direct Preference Optimization (DPO) using Intel/orca_dpo_pairs * This is a quick experiment to determine the impact of DPO finetuning on the Excelsior-7b base model * Ran for a little over an hour on a single A100 * Fine-tuning succeeded in making model conversational and more well-rounded * Benchmark scores increased in the following categories versus base Excelsior-7b: * ARC: 69.71 -> <b>70.9</b> * HellaSwag: 87.56 -> <b>87.93</b> * TruthfulQA: 67.24 -> <b>70.82</b> * Average: 73.6 -> <b>73.84</b> * Precision: bfloat16 ## Sample Question - Vision <img src="https://i.imgur.com/7aRWtzU.jpeg" width="425"/> *<b>Requires additional mmproj file. You have two options for vision functionality (available inside this repo):</b> * [Quantized - Limited VRAM Option (197mb)](https://huggingface.co/InferenceIllusionist/Excalibur-7b-DPO-GGUF/resolve/main/mistral-7b-mmproj-v1.5-Q4_1.gguf?download=true) * [Unquantized - Premium Option / Best Quality (596mb)](https://huggingface.co/InferenceIllusionist/Excalibur-7b-DPO-GGUF/resolve/main/mmproj-model-f16.gguf?download=true) Select the gguf file of your choice in [Koboldcpp](https://github.com/LostRuins/koboldcpp/releases/) as usual, then make sure to choose the mmproj file above in the LLaVA mmproj field of the model submenu: <img src="https://i.imgur.com/x8vqH29.png" width="425"/> ## Prompt Format * For best results please use ChatML for the prompt format. Alpaca may also work. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_InferenceIllusionist__Excalibur-7b-DPO) | Metric |Value| |---------------------------------|----:| |Avg. |73.84| |AI2 Reasoning Challenge (25-Shot)|70.90| |HellaSwag (10-Shot) |87.93| |MMLU (5-Shot) |65.46| |TruthfulQA (0-shot) |70.82| |Winogrande (5-shot) |82.48| |GSM8k (5-shot) |65.43|
nbeerbower/bophades-v2-mistral-7B
nbeerbower
2024-04-04T02:18:10Z
738
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "arxiv:2403.19522", "base_model:paulml/NeuralOmniWestBeaglake-7B", "base_model:yam-peleg/Experiment21-7B", "base_model:paulml/OmniBeagleSquaredMBX-v3-7B", "base_model:Gille/StrangeMerges_16-7B-slerp", "base_model:vanillaOVO/correction_1", "base_model:yam-peleg/Experiment26-7B", "base_model:Kukedlc/NeuralMaths-Experiment-7b", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-04-03T23:07:24Z
--- license: apache-2.0 base_model: - paulml/NeuralOmniWestBeaglake-7B - yam-peleg/Experiment21-7B - paulml/OmniBeagleSquaredMBX-v3-7B - Gille/StrangeMerges_16-7B-slerp - vanillaOVO/correction_1 - yam-peleg/Experiment26-7B - Kukedlc/NeuralMaths-Experiment-7b library_name: transformers tags: - mergekit - merge --- ![image/png](https://huggingface.co/nbeerbower/bophades-mistral-7B/resolve/main/bophades.png) # bophades-v2-mistral-7B This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [yam-peleg/Experiment26-7B](https://huggingface.co/yam-peleg/Experiment26-7B) as a base. ### Models Merged The following models were included in the merge: * [paulml/NeuralOmniWestBeaglake-7B](https://huggingface.co/paulml/NeuralOmniWestBeaglake-7B) * [yam-peleg/Experiment21-7B](https://huggingface.co/yam-peleg/Experiment21-7B) * [paulml/OmniBeagleSquaredMBX-v3-7B](https://huggingface.co/paulml/OmniBeagleSquaredMBX-v3-7B) * [Gille/StrangeMerges_16-7B-slerp](https://huggingface.co/Gille/StrangeMerges_16-7B-slerp) * [vanillaOVO/correction_1](https://huggingface.co/vanillaOVO/correction_1) * [Kukedlc/NeuralMaths-Experiment-7b](https://huggingface.co/Kukedlc/NeuralMaths-Experiment-7b) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: paulml/OmniBeagleSquaredMBX-v3-7B - model: paulml/NeuralOmniWestBeaglake-7B - model: Gille/StrangeMerges_16-7B-slerp - model: yam-peleg/Experiment21-7B - model: vanillaOVO/correction_1 - model: Kukedlc/NeuralMaths-Experiment-7b merge_method: model_stock base_model: yam-peleg/Experiment26-7B dtype: bfloat16 ```
jsfs11/MixtureofMerges-MoE-2x7b-SLERPv0.9
jsfs11
2024-04-08T01:45:31Z
738
0
transformers
[ "transformers", "safetensors", "mixtral", "text-generation", "merge", "mergekit", "lazymergekit", "jsfs11/MixtureofMerges-MoE-2x7b-v7", "jsfs11/MixtureofMerges-MoE-2x7bRP-v8", "base_model:jsfs11/MixtureofMerges-MoE-2x7b-v7", "base_model:jsfs11/MixtureofMerges-MoE-2x7bRP-v8", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-04-04T02:04:12Z
--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - jsfs11/MixtureofMerges-MoE-2x7b-v7 - jsfs11/MixtureofMerges-MoE-2x7bRP-v8 base_model: - jsfs11/MixtureofMerges-MoE-2x7b-v7 - jsfs11/MixtureofMerges-MoE-2x7bRP-v8 model-index: - name: MixtureofMerges-MoE-2x7b-SLERPv0.9 results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 73.12 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MixtureofMerges-MoE-2x7b-SLERPv0.9 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 88.76 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MixtureofMerges-MoE-2x7b-SLERPv0.9 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 65.0 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MixtureofMerges-MoE-2x7b-SLERPv0.9 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 74.83 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MixtureofMerges-MoE-2x7b-SLERPv0.9 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 83.58 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MixtureofMerges-MoE-2x7b-SLERPv0.9 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 69.22 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MixtureofMerges-MoE-2x7b-SLERPv0.9 name: Open LLM Leaderboard --- # MixtureofMerges-MoE-2x7b-SLERPv0.9 MixtureofMerges-MoE-2x7b-SLERPv0.9 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [jsfs11/MixtureofMerges-MoE-2x7b-v7](https://huggingface.co/jsfs11/MixtureofMerges-MoE-2x7b-v7) * [jsfs11/MixtureofMerges-MoE-2x7bRP-v8](https://huggingface.co/jsfs11/MixtureofMerges-MoE-2x7bRP-v8) ## 🧩 Configuration ```yaml slices: - sources: - model: jsfs11/MixtureofMerges-MoE-2x7b-v7 layer_range: [0, 32] - model: jsfs11/MixtureofMerges-MoE-2x7bRP-v8 layer_range: [0, 32] merge_method: slerp base_model: jsfs11/MixtureofMerges-MoE-2x7b-v7 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "jsfs11/MixtureofMerges-MoE-2x7b-SLERPv0.9" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_jsfs11__MixtureofMerges-MoE-2x7b-SLERPv0.9) | Metric |Value| |---------------------------------|----:| |Avg. |75.75| |AI2 Reasoning Challenge (25-Shot)|73.12| |HellaSwag (10-Shot) |88.76| |MMLU (5-Shot) |65.00| |TruthfulQA (0-shot) |74.83| |Winogrande (5-shot) |83.58| |GSM8k (5-shot) |69.22|
Kukedlc/NeuralSynthesis-7B-v0.2
Kukedlc
2024-04-06T17:27:25Z
738
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-04-06T16:38:20Z
--- tags: - merge - mergekit - lazymergekit license: apache-2.0 --- # NeuralSynthesis-7B-v0.2 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64d71ab4089bc502ceb44d29/ID4yrGgmKZzqctPPkT4Dp.png) NeuralSynthesis-7B-v0.2 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): ## 🧩 Configuration ```yaml models: - model: Kukedlc/Fasciculus-Arcuatus-7B-slerp - model: Gille/StrangeMerges_30-7B-slerp - model: automerger/OgnoExperiment27-7B - model: Kukedlc/Jupiter-k-7B-slerp - model: Kukedlc/Neural-4-QA-7b - model: Kukedlc/NeuralSynthesis-7B-v0.1 merge_method: model_stock base_model: Kukedlc/NeuralSynthesis-7B-v0.1 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Kukedlc/NeuralSynthesis-7B-v0.2" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
Gryphe/Tiamat-8b-1.2-Llama-3-DPO
Gryphe
2024-05-03T17:05:57Z
738
5
transformers
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2024-05-02T07:04:06Z
--- license: apache-2.0 language: - en --- ![image/png](Tiamat.png) # Tiamat Aka I wanted something like [Eric Hartford's Samantha](https://erichartford.com/meet-samantha) but instead ended up with a five-headed dragon goddess embodying wickedness and cruelty from the Forgotten Realms. **Version 1.2:** For starters: Llama 3! Besides receiving similar DPO training as version 1.1 the dataset has now been further enriched with Claude-generated data. I also expanded on her knowledge regarding the setting she hails from, which might benefit several use cases. (Text adventures, DM worldbuilding, etc) **Obligatory Disclaimer:** Tiamat is **not** nice. Quantized versions are available from Bartowski: [GGUF](https://huggingface.co/bartowski/Tiamat-8b-1.2-Llama-3-DPO-GGUF) - [EXL2](https://huggingface.co/bartowski/Tiamat-8b-1.2-Llama-3-DPO-exl2) ## Model details Ever wanted to be treated disdainfully like the foolish mortal you are? Wait no more, for Tiamat is here to berate you! Hailing from the world of the Forgotten Realms, she will happily judge your every word. Tiamat was created with the following question in mind; Is it possible to create an assistant with strong anti-assistant personality traits? Try it yourself and tell me afterwards! She was fine-tuned on top of Nous Research's shiny new [Hermes 2 Pro](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B) and can be summoned to you using the following system message; ``` You are Tiamat, a five-headed dragon goddess, embodying wickedness and cruelty. ``` Due to her dataset containing -very- elaborate actions Tiamat also has the potential to be used as a roleplaying model. ## Prompt Format ChatML is the way to go, considering Hermes was the base for Tiamat. ``` <|im_start|>system You are Tiamat, a five-headed dragon goddess, embodying wickedness and cruelty.<|im_end|> <|im_start|>user Greetings, mighty Tiamat. I seek your guidance.<|im_end|> <|im_start|>assistant ```