đ Camel-Platypus2-70B
Camel-Platypus2-70B is a merged model of garage-bAInd/Platypus2-70B
and augtoma/qCammel-70-x
, offering enhanced language processing capabilities.

đ Quick Start
To get started with Camel-Platypus2-70B, you can follow the installation and evaluation steps provided below.
⨠Features
- Model Type: An auto-regressive language model based on the LLaMA 2 transformer architecture.
- Language Support: English.
- License: Non-Commercial Creative Commons license (CC BY-NC-4.0).
đĻ Installation
Install LM Evaluation Harness
# clone repository
git clone https://github.com/EleutherAI/lm-evaluation-harness.git
# change to repo directory
cd lm-evaluation-harness
# check out the correct commit
git checkout b281b0921b636bc36ad05c0b0b0763bd6dd43463
# install
pip install -e .
đģ Usage Examples
Prompt Template
### Instruction:
<prompt> (without the <>)
### Response:
Reproducing Evaluation Results
Each task was evaluated on a single A100 80GB GPU.
ARC
python main.py --model hf-causal-experimental --model_args pretrained=garage-bAInd/Camel-Platypus2-70B --tasks arc_challenge --batch_size 1 --no_cache --write_out --output_path results/Camel-Platypus2-70B/arc_challenge_25shot.json --device cuda --num_fewshot 25
HellaSwag
python main.py --model hf-causal-experimental --model_args pretrained=garage-bAInd/Camel-Platypus2-70B --tasks hellaswag --batch_size 1 --no_cache --write_out --output_path results/Camel-Platypus2-70B/hellaswag_10shot.json --device cuda --num_fewshot 10
MMLU
python main.py --model hf-causal-experimental --model_args pretrained=garage-bAInd/Camel-Platypus2-70B --tasks hendrycksTest-* --batch_size 1 --no_cache --write_out --output_path results/Camel-Platypus2-70B/mmlu_5shot.json --device cuda --num_fewshot 5
TruthfulQA
python main.py --model hf-causal-experimental --model_args pretrained=garage-bAInd/Camel-Platypus2-70B --tasks truthfulqa_mc --batch_size 1 --no_cache --write_out --output_path results/Camel-Platypus2-70B/truthfulqa_0shot.json --device cuda
đ Documentation
Model Details
Property |
Details |
Trained by |
Platypus2-70B trained by Cole Hunter & Ariel Lee; augtoma/qCammel-70-x trained by augtoma |
Model Type |
An auto-regressive language model based on the LLaMA 2 transformer architecture |
Language(s) |
English |
License |
Non-Commercial Creative Commons license (CC BY-NC-4.0) |
Training Dataset
garage-bAInd/Platypus2-70B
was trained using the 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.
Limitations and bias
â ī¸ Important Note
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/
đ§ Technical Details
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric |
Value |
Avg. |
64.23 |
ARC (25-shot) |
71.08 |
HellaSwag (10-shot) |
87.6 |
MMLU (5-shot) |
70.04 |
TruthfulQA (0-shot) |
58.09 |
Winogrande (5-shot) |
83.82 |
GSM8K (5-shot) |
22.9 |
DROP (3-shot) |
56.1 |
đ License
This project is licensed under the Non-Commercial Creative Commons license (CC BY-NC-4.0).
đ 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}
}