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2024-11-4 LLaMA-O1 is Opensource Now! Call for Contributors! https://github.com/SimpleBerry/LLaMA-O1 https://huggingface.co/datasets/qq8933/OpenLongCoT-Pretrain https://huggingface.co/datasets/qq8933/OpenLongCoT-SFT
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2024-10-12 🚀 Exciting News! At the end of October, we’re announcing the next phase of our work on the open-source reimplementation of OpenAI O1, codenamed TiC!
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2024-10-11 New Preprint! LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning https://arxiv.org/abs/2410.02884
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2024-09-30 🚀 Exciting News! At the end of September, we're releasing an upgraded version of the MCTSr algorithm, LLaMA-Berry, as an open-source fork of the OpenAI Strawberry project. This enhanced tool specializes in tackling the most challenging Mathematical Olympiad tasks
We need VLLM or other Openai compatible method.
pip install vllm
We need Huggingface toolkit and Openai for inference.
pip install datasets transformers openai
The script relies on Slurm, If you run it on non-slurm environments,
Just use VLLM to create a openai compatible server, and insert to 'server.csv'
IP,PORT,MODEL_NAME
If you run it on slurm environment, change the partition name to your own partition in make_n_server.py
then, you can run the run_with_earlystopping.py for datasets.
python run_with_earlystopping.py MODEL_NAME DATA_DIR_NAME
datasets were given by the first part of DATA_DIR_NAME arguments, like gsm8k-llama3-8b-new-mcts-8 for gsm8k , can selected in,
'gsm8k-llama3-8b-new-mcts-8',
'gsmhard-llama3-8b-new-mcts-8',
'olympiadbench-llama3-8b-new-mcts-8',
'GAIC-llama3-8b-new-mcts-8',
'MATH-llama3-8b-new-mcts-8',
'AIME-llama3-8b-mcts-2'
Using run_olympics.py to run all of them.
Alert: That would consume a long time.
@misc{zhang2024accessing,
title={Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B},
author={Di Zhang and Xiaoshui Huang and Dongzhan Zhou and Yuqiang Li and Wanli Ouyang},
year={2024},
eprint={2406.07394},
archivePrefix={arXiv},
primaryClass={id='cs.AI' full_name='Artificial Intelligence' is_active=True alt_name=None in_archive='cs' is_general=False description='Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.'}
}
@misc{zhang2024llamaberrypairwiseoptimizationo1like,
title={LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning},
author={Di Zhang and Jianbo Wu and Jingdi Lei and Tong Che and Jiatong Li and Tong Xie and Xiaoshui Huang and Shufei Zhang and Marco Pavone and Yuqiang Li and Wanli Ouyang and Dongzhan Zhou},
year={2024},
eprint={2410.02884},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2410.02884},
}
This project was still in a very early stage for explore, pay attentions for the algorithm's output, and do not deploying it to real-world product without fully test.
This repository was for personal experimentation only and has no connection with any third-party organization or institution.
https://arxiv.org/abs/2406.07394