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Platypus2-13B - GGUF

• Model creator: https://huggingface.co/garage-bAInd/
• Original model: https://huggingface.co/garage-bAInd/Platypus2-13B/

Name	Quant method	Size
Platypus2-13B.Q2_K.gguf	Q2_K	4.52GB
Platypus2-13B.IQ3_XS.gguf	IQ3_XS	4.99GB
Platypus2-13B.IQ3_S.gguf	IQ3_S	5.27GB
Platypus2-13B.Q3_K_S.gguf	Q3_K_S	5.27GB
Platypus2-13B.IQ3_M.gguf	IQ3_M	5.57GB
Platypus2-13B.Q3_K.gguf	Q3_K	5.9GB
Platypus2-13B.Q3_K_M.gguf	Q3_K_M	5.9GB
Platypus2-13B.Q3_K_L.gguf	Q3_K_L	6.45GB
Platypus2-13B.IQ4_XS.gguf	IQ4_XS	6.54GB
Platypus2-13B.Q4_0.gguf	Q4_0	6.86GB
Platypus2-13B.IQ4_NL.gguf	IQ4_NL	6.9GB
Platypus2-13B.Q4_K_S.gguf	Q4_K_S	6.91GB
Platypus2-13B.Q4_K.gguf	Q4_K	7.33GB
Platypus2-13B.Q4_K_M.gguf	Q4_K_M	7.33GB
Platypus2-13B.Q4_1.gguf	Q4_1	7.61GB
Platypus2-13B.Q5_0.gguf	Q5_0	8.36GB
Platypus2-13B.Q5_K_S.gguf	Q5_K_S	8.36GB
Platypus2-13B.Q5_K.gguf	Q5_K	8.6GB
Platypus2-13B.Q5_K_M.gguf	Q5_K_M	8.6GB
Platypus2-13B.Q5_1.gguf	Q5_1	9.1GB
Platypus2-13B.Q6_K.gguf	Q6_K	9.95GB
Platypus2-13B.Q8_0.gguf	Q8_0	12.88GB

Original model description:

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

• en datasets:
• garage-bAInd/Open-Platypus

---

Platypus2-13B

Platypus-13B is an instruction fine-tuned model based on the LLaMA2-13B transformer architecture.

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:

<prompt> (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

@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.	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