Quantization made by Richard Erkhov.

Platypus2-70B - GGUF

Model creator: https://huggingface.co/garage-bAInd/
Original model: https://huggingface.co/garage-bAInd/Platypus2-70B/

Name	Quant method	Size
Platypus2-70B.Q2_K.gguf	Q2_K	23.71GB
Platypus2-70B.IQ3_XS.gguf	IQ3_XS	26.37GB
Platypus2-70B.IQ3_S.gguf	IQ3_S	27.86GB
Platypus2-70B.Q3_K_S.gguf	Q3_K_S	27.86GB
Platypus2-70B.IQ3_M.gguf	IQ3_M	28.82GB
Platypus2-70B.Q3_K.gguf	Q3_K	30.99GB
Platypus2-70B.Q3_K_M.gguf	Q3_K_M	30.99GB
Platypus2-70B.Q3_K_L.gguf	Q3_K_L	33.67GB
Platypus2-70B.IQ4_XS.gguf	IQ4_XS	34.64GB
Platypus2-70B.Q4_0.gguf	Q4_0	36.2GB
Platypus2-70B.IQ4_NL.gguf	IQ4_NL	36.55GB
Platypus2-70B.Q4_K_S.gguf	Q4_K_S	36.55GB
Platypus2-70B.Q4_K.gguf	Q4_K	38.58GB
Platypus2-70B.Q4_K_M.gguf	Q4_K_M	38.58GB
Platypus2-70B.Q4_1.gguf	Q4_1	40.2GB
Platypus2-70B.Q5_0.gguf	Q5_0	44.2GB
Platypus2-70B.Q5_K_S.gguf	Q5_K_S	44.2GB
Platypus2-70B.Q5_K.gguf	Q5_K	45.41GB
Platypus2-70B.Q5_K_M.gguf	Q5_K_M	45.41GB
Platypus2-70B.Q5_1.gguf	Q5_1	48.2GB
Platypus2-70B.Q6_K.gguf	Q6_K	52.7GB
Platypus2-70B.Q8_0.gguf	Q8_0	68.26GB

Original model description:

license: cc-by-nc-sa-4.0 language: - en datasets: - garage-bAInd/Open-Platypus

Platypus2-70B

Platypus-70B is an instruction fine-tuned model based on the LLaMa2-70B transformer architecture.

Model Details

Trained by: Cole Hunter & Ariel Lee
Model type: Platypus2-70B 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:

<prompt> (without the <>)

### Response:

Training Dataset

garage-bAInd/Platypus2-70B 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/Platypus2-70B was instruction fine-tuned using LoRA on 8 A100 80GB. For training details and inference instructions please see the Platypus 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 a single A100 80GB GPU.

ARC:

python main.py --model hf-causal-experimental --model_args pretrained=garage-bAInd/Platypus2-70B --tasks arc_challenge --batch_size 1 --no_cache --write_out --output_path results/Platypus2-70B/arc_challenge_25shot.json --device cuda --num_fewshot 25

HellaSwag:

python main.py --model hf-causal-experimental --model_args pretrained=garage-bAInd/Platypus2-70B --tasks hellaswag --batch_size 1 --no_cache --write_out --output_path results/Platypus2-70B/hellaswag_10shot.json --device cuda --num_fewshot 10

MMLU:

python main.py --model hf-causal-experimental --model_args pretrained=garage-bAInd/Platypus2-70B --tasks hendrycksTest-* --batch_size 1 --no_cache --write_out --output_path results/Platypus2-70B/mmlu_5shot.json --device cuda --num_fewshot 5

TruthfulQA:

python main.py --model hf-causal-experimental --model_args pretrained=garage-bAInd/Platypus2-70B --tasks truthfulqa_mc --batch_size 1 --no_cache --write_out --output_path results/Platypus2-70B/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

@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}
}

@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},
}

@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.	64.16
ARC (25-shot)	70.65
HellaSwag (10-shot)	87.15
MMLU (5-shot)	70.08
TruthfulQA (0-shot)	52.37
Winogrande (5-shot)	84.37
GSM8K (5-shot)	33.06
DROP (3-shot)	51.41