Hugging Face's logo

RichardErkhov
/
garage-bAInd_-_Platypus2-70B-gguf

GGUF
Inference Endpoints
Model card Files Community
garage-bAInd_-_Platypus2-70B-gguf / README.md
RichardErkhov's picture
RichardErkhov
uploaded readme
902a52b verified
preview code
|
raw
history blame contribute delete
8.6 kB

Quantization made by Richard Erkhov.

Github

Discord

Request more models

Platypus2-70B - GGUF

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.

Platty

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