Awesome
MCTSr: Mathematic as a Blackbox for LLM
Testing with GPT-4o-mini
Adapted from here.
Prep
Set the OPENAI_API_KEY in your environment
export OPENAI_API_KEY = <key>
Create the Python virtual environment
python -m venv venv
Run a benchmark, say, gsm8k
python run_with_earlystopping.py gpt-4o-mini gsm8k-gpt-4o-mini
Results saved in the folder name provided as second param above.
Check the accuracy
python anal.py gsm8k-gpt-4o-mini gsm8k-gpt-4o-mini
Previous instructions below
Server
We need VLLM or other Openai compatible method.
pip install vllm
Clients
We need Huggingface toolkit and Openai for inference.
pip install datasets transformers openai
Usage
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
Support Datasets
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.
Citation
@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.'}
}
Disclaimer
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.
Read More
https://arxiv.org/abs/2406.07394