richarderkhov/garage-baind_-_platypus2-13b-gguf overview

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    "summary": "Platypus-13B is an instruction fine-tuned model based on the LLaMA2-13B transformer architecture. !Platty ### Model Details * **Trained by**: Cole Hunter & Ariel Lee * **Model type:**  **Platypus2-13B** is an auto-regressive language model based on the LLaMA2 transformer architecture. * **Language(s)**: English * **License for base weights**: Non-Commercial Creative Commons license (CC BY-NC-4.0) ### Prompt Template `` ### Instruction:  (without the <>) ### Response: ` ### Training Dataset garage-bAInd/Platypus2-13B trained using STEM and logic based dataset garage-bAInd/Open-Platypus. Please see our paper and project webpage for additional information. ### Training Procedure garage-bAInd/Platypus2-13B was instruction fine-tuned using LoRA on 1 A100 80GB. For training details and inference instructions please see the Platypus2 GitHub repo. ### Reproducing Evaluation Results Install LM Evaluation Harness: ` # clone repository git clone https://github.com/EleutherAI/lm-evaluation-harness.git # check out the correct commit git checkout b281b0921b636bc36ad05c0b0b0763bd6dd43463 # change to repo directory cd lm-evaluation-harness # install pip install -e . ` Each task was evaluated on 1 A100 80GB GPU. ARC: ` python main.py --model hf-causal-experimental --model_args pretrained=garage-bAInd/Platypus2-13B --tasks arc_challenge --batch_size 1 --no_cache --write_out --output_path results/Platypus2-13B/arc_challenge_25shot.json --device cuda --num_fewshot 25 ` HellaSwag: ` python main.py --model hf-causal-experimental --model_args pretrained=garage-bAInd/Platypus2-13B --tasks hellaswag --batch_size 1 --no_cache --write_out --output_path results/Platypus2-13B/hellaswag_10shot.json --device cuda --num_fewshot 10 ` MMLU: ` python main.py --model hf-causal-experimental --model_args pretrained=garage-bAInd/Platypus2-13B --tasks hendrycksTest-* --batch_size 1 --no_cache --write_out --output_path results/Platypus2-13B/mmlu_5shot.json --device cuda --num_fewshot 5 ` TruthfulQA: ` python main.py --model hf-causal-experimental --model_args pretrained=garage-bAInd/Platypus2-13B --tasks truthfulqa_mc --batch_size 1 --no_cache --write_out --output_path results/Platypus2-13B/truthfulqa_0shot.json --device cuda ` ### Limitations and bias Llama 2 and fine-tuned variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2 and any fine-tuned varient's potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2 variants, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available at https://ai.meta.com/llama/responsible-use-guide/ ### Citations `bibtex @article{platypus2023, title={Platypus: Quick, Cheap, and Powerful Refinement of LLMs}, author={Ariel N. Lee and Cole J. Hunter and Nataniel Ruiz}, booktitle={arXiv preprint arxiv:2308.07317}, year={2023} } ` `bibtex @misc{touvron2023llama, title={Llama 2: Open Foundation and Fine-Tuned Chat Models}, author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov       year={2023}, eprint={2307.09288}, archivePrefix={arXiv}, } ` `bibtex @inproceedings{ hu2022lora, title={Lo{RA}: Low-Rank Adaptation of Large Language Models}, author={Edward J Hu and Yelong Shen and Phillip Wallis and Zeyuan Allen-Zhu and Yuanzhi Li and Shean Wang and Lu Wang and Weizhu Chen}, booktitle={International Conference on Learning Representations}, year={2022}, url={https://openreview.net/forum?id=nZeVKeeFYf9} } `` # Open LLM Leaderboard Evaluation Results Detailed results can be found here | Metric                | Value                     | |-----------------------|---------------------------| | Avg.                  | 48.04   | | ARC (25-shot)         | 61.26          | | HellaSwag (10-shot)   | 82.56    | | MMLU (5-shot)         | 56.7         | | TruthfulQA (0-shot)   | 44.86   | | Winogrande (5-shot)   | 76.87   | | GSM8K (5-shot)        | 7.05        | | DROP (3-shot)         | 6.95         |",
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    "readme_markdown": "Quantization made by Richard Erkhov.\n\n[Github](https://github.com/RichardErkhov)\n\n[Discord](https://discord.gg/pvy7H8DZMG)\n\n[Request more models](https://github.com/RichardErkhov/quant_request)\n\n\nPlatypus2-13B - GGUF\n- Model creator: https://huggingface.co/garage-bAInd/\n- Original model: https://huggingface.co/garage-bAInd/Platypus2-13B/\n\n\n| Name | Quant method | Size |\n| ---- | ---- | ---- |\n| [Platypus2-13B.Q2_K.gguf](https://huggingface.co/RichardErkhov/garage-bAInd_-_Platypus2-13B-gguf/blob/main/Platypus2-13B.Q2_K.gguf) | Q2_K | 4.52GB |\n| [Platypus2-13B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/garage-bAInd_-_Platypus2-13B-gguf/blob/main/Platypus2-13B.IQ3_XS.gguf) | IQ3_XS | 4.99GB |\n| [Platypus2-13B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/garage-bAInd_-_Platypus2-13B-gguf/blob/main/Platypus2-13B.IQ3_S.gguf) | IQ3_S | 5.27GB |\n| [Platypus2-13B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/garage-bAInd_-_Platypus2-13B-gguf/blob/main/Platypus2-13B.Q3_K_S.gguf) | Q3_K_S | 5.27GB |\n| [Platypus2-13B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/garage-bAInd_-_Platypus2-13B-gguf/blob/main/Platypus2-13B.IQ3_M.gguf) | IQ3_M | 5.57GB |\n| [Platypus2-13B.Q3_K.gguf](https://huggingface.co/RichardErkhov/garage-bAInd_-_Platypus2-13B-gguf/blob/main/Platypus2-13B.Q3_K.gguf) | Q3_K | 5.9GB |\n| [Platypus2-13B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/garage-bAInd_-_Platypus2-13B-gguf/blob/main/Platypus2-13B.Q3_K_M.gguf) | Q3_K_M | 5.9GB |\n| [Platypus2-13B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/garage-bAInd_-_Platypus2-13B-gguf/blob/main/Platypus2-13B.Q3_K_L.gguf) | Q3_K_L | 6.45GB |\n| [Platypus2-13B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/garage-bAInd_-_Platypus2-13B-gguf/blob/main/Platypus2-13B.IQ4_XS.gguf) | IQ4_XS | 6.54GB |\n| [Platypus2-13B.Q4_0.gguf](https://huggingface.co/RichardErkhov/garage-bAInd_-_Platypus2-13B-gguf/blob/main/Platypus2-13B.Q4_0.gguf) | Q4_0 | 6.86GB |\n| [Platypus2-13B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/garage-bAInd_-_Platypus2-13B-gguf/blob/main/Platypus2-13B.IQ4_NL.gguf) | IQ4_NL | 6.9GB |\n| [Platypus2-13B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/garage-bAInd_-_Platypus2-13B-gguf/blob/main/Platypus2-13B.Q4_K_S.gguf) | Q4_K_S | 6.91GB |\n| [Platypus2-13B.Q4_K.gguf](https://huggingface.co/RichardErkhov/garage-bAInd_-_Platypus2-13B-gguf/blob/main/Platypus2-13B.Q4_K.gguf) | Q4_K | 7.33GB |\n| [Platypus2-13B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/garage-bAInd_-_Platypus2-13B-gguf/blob/main/Platypus2-13B.Q4_K_M.gguf) | Q4_K_M | 7.33GB |\n| [Platypus2-13B.Q4_1.gguf](https://huggingface.co/RichardErkhov/garage-bAInd_-_Platypus2-13B-gguf/blob/main/Platypus2-13B.Q4_1.gguf) | Q4_1 | 7.61GB |\n| [Platypus2-13B.Q5_0.gguf](https://huggingface.co/RichardErkhov/garage-bAInd_-_Platypus2-13B-gguf/blob/main/Platypus2-13B.Q5_0.gguf) | Q5_0 | 8.36GB |\n| [Platypus2-13B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/garage-bAInd_-_Platypus2-13B-gguf/blob/main/Platypus2-13B.Q5_K_S.gguf) | Q5_K_S | 8.36GB |\n| [Platypus2-13B.Q5_K.gguf](https://huggingface.co/RichardErkhov/garage-bAInd_-_Platypus2-13B-gguf/blob/main/Platypus2-13B.Q5_K.gguf) | Q5_K | 8.6GB |\n| [Platypus2-13B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/garage-bAInd_-_Platypus2-13B-gguf/blob/main/Platypus2-13B.Q5_K_M.gguf) | Q5_K_M | 8.6GB |\n| [Platypus2-13B.Q5_1.gguf](https://huggingface.co/RichardErkhov/garage-bAInd_-_Platypus2-13B-gguf/blob/main/Platypus2-13B.Q5_1.gguf) | Q5_1 | 9.1GB |\n| [Platypus2-13B.Q6_K.gguf](https://huggingface.co/RichardErkhov/garage-bAInd_-_Platypus2-13B-gguf/blob/main/Platypus2-13B.Q6_K.gguf) | Q6_K | 9.95GB |\n| [Platypus2-13B.Q8_0.gguf](https://huggingface.co/RichardErkhov/garage-bAInd_-_Platypus2-13B-gguf/blob/main/Platypus2-13B.Q8_0.gguf) | Q8_0 | 12.88GB |\n\n\n\n\nOriginal model description:\n---\nlicense: cc-by-nc-sa-4.0\nlanguage:\n- en\ndatasets:\n- garage-bAInd/Open-Platypus\n---\n\n# Platypus2-13B\n\nPlatypus-13B is an instruction fine-tuned model based on the LLaMA2-13B transformer architecture.\n\n![Platty](./Best_Platty_small.jpeg)\n\n### Model Details\n\n* **Trained by**: Cole Hunter & Ariel Lee\n* **Model type:**  **Platypus2-13B** is an auto-regressive language model based on the LLaMA2 transformer architecture.\n* **Language(s)**: English\n* **License for base weights**: Non-Commercial Creative Commons license ([CC BY-NC-4.0](https://creativecommons.org/licenses/by-nc/4.0/))\n\n### Prompt Template\n```\n### Instruction:\n\n<prompt> (without the <>)\n\n### Response:\n```\n\n### Training Dataset\n\n`garage-bAInd/Platypus2-13B` trained using STEM and logic based dataset [`garage-bAInd/Open-Platypus`](https://huggingface.co/datasets/garage-bAInd/Open-Platypus).\n\nPlease see our [paper](https://arxiv.org/abs/2308.07317) and [project webpage](https://platypus-llm.github.io) for additional information.\n\n### Training Procedure\n\n`garage-bAInd/Platypus2-13B` was instruction fine-tuned using LoRA on 1 A100 80GB. For training details and inference instructions please see the [Platypus2](https://github.com/arielnlee/Platypus) GitHub repo.\n\n### Reproducing Evaluation Results\n\nInstall LM Evaluation Harness:\n```\n# clone repository\ngit clone https://github.com/EleutherAI/lm-evaluation-harness.git\n# check out the correct commit\ngit checkout b281b0921b636bc36ad05c0b0b0763bd6dd43463\n# change to repo directory\ncd lm-evaluation-harness\n# install\npip install -e .\n```\nEach task was evaluated on 1 A100 80GB GPU.\n\nARC:\n```\npython main.py --model hf-causal-experimental --model_args pretrained=garage-bAInd/Platypus2-13B --tasks arc_challenge --batch_size 1 --no_cache --write_out --output_path results/Platypus2-13B/arc_challenge_25shot.json --device cuda --num_fewshot 25\n```\n\nHellaSwag:\n```\npython main.py --model hf-causal-experimental --model_args pretrained=garage-bAInd/Platypus2-13B --tasks hellaswag --batch_size 1 --no_cache --write_out --output_path results/Platypus2-13B/hellaswag_10shot.json --device cuda --num_fewshot 10\n```\n\nMMLU:\n```\npython main.py --model hf-causal-experimental --model_args pretrained=garage-bAInd/Platypus2-13B --tasks hendrycksTest-* --batch_size 1 --no_cache --write_out --output_path results/Platypus2-13B/mmlu_5shot.json --device cuda --num_fewshot 5\n```\n\nTruthfulQA:\n```\npython main.py --model hf-causal-experimental --model_args pretrained=garage-bAInd/Platypus2-13B --tasks truthfulqa_mc --batch_size 1 --no_cache --write_out --output_path results/Platypus2-13B/truthfulqa_0shot.json --device cuda\n```\n### Limitations and bias\n\nLlama 2 and fine-tuned variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2 and any fine-tuned varient's potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2 variants, developers should perform safety testing and tuning tailored to their specific applications of the model.\n\nPlease see the Responsible Use Guide available at https://ai.meta.com/llama/responsible-use-guide/\n\n### Citations\n```bibtex\n@article{platypus2023,\n    title={Platypus: Quick, Cheap, and Powerful Refinement of LLMs}, \n    author={Ariel N. Lee and Cole J. Hunter and Nataniel Ruiz},\n    booktitle={arXiv preprint arxiv:2308.07317},\n    year={2023}\n}\n```\n```bibtex\n@misc{touvron2023llama,\n    title={Llama 2: Open Foundation and Fine-Tuned Chat Models}, \n    author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov       year={2023},\n    eprint={2307.09288},\n    archivePrefix={arXiv},\n}\n```\n```bibtex\n@inproceedings{\n    hu2022lora,\n    title={Lo{RA}: Low-Rank Adaptation of Large Language Models},\n    author={Edward J Hu and Yelong Shen and Phillip Wallis and Zeyuan Allen-Zhu and Yuanzhi Li and Shean Wang and Lu Wang and Weizhu Chen},\n    booktitle={International Conference on Learning Representations},\n    year={2022},\n    url={https://openreview.net/forum?id=nZeVKeeFYf9}\n}\n```\n# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)\nDetailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_garage-bAInd__Platypus2-13B)\n\n| Metric                | Value                     |\n|-----------------------|---------------------------|\n| Avg.                  | 48.04   |\n| ARC (25-shot)         | 61.26          |\n| HellaSwag (10-shot)   | 82.56    |\n| MMLU (5-shot)         | 56.7         |\n| TruthfulQA (0-shot)   | 44.86   |\n| Winogrande (5-shot)   | 76.87   |\n| GSM8K (5-shot)        | 7.05        |\n| DROP (3-shot)         | 6.95         |\n\n\n",
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