This forked repository contains modified scripts to run rStar's generator and discriminator on the dair-ai/emotion dataset.
- Python 3.10
- CUDA 12
- newest PyTorch
- newest
transformers - newest
vllm
The only change to the default usage options below is to change the --dataset_name flag to Emotion
Here is an example to run rStar generator:
bash scripts/run_gsm8k_generator.shThe script run_gsm8k_generator.sh includes several configurable parameters:
--dataset_name: Name of the dataset (choose from [MATH, GSM8K, GSM8KHARD, STG, SVAMP, MULTIARITH]).--test_json_filename: Filename for the test JSON (default: test_all).--model_ckpt: Path to the model checkpoint.--note: Additional note to mark the output folder. Without further arguments, the generator output folder will be./run_outputs/<dataset_name>/<model_ckpt>/<execute_time>---[<note>]--num_rollouts: Number of rollouts (default: 16).
Make sure to adjust these parameters according to your requirements.
Here is an example to evalute the results of rStar generator
python eval_src/do_eval.py --dataset_name GSM8K --exp_dir_path <generator_output_folder>Here is an example to run rStar discriminator:
bash scripts/run_gsm8k_discriminator.shThe script run_gsm8k_discriminator.sh includes several configurable parameters:
--model_ckpt: checkpoint Path to the discriminator model.--root_dir: Path of evalution result folder.--dataset_name: Name of the dataset (choose from [MATH, GSM8K, GSM8KHARD, STG, SVAMP, MULTIARITH]).--note: Additional note to mark the output folder. Without further arguments, the discriminator output folder will be<root_dir>/dis_<execute_time>---<note>
Extensive experiments across five SLMs demonstrate rStar can effectively solve diverse reasoning problems. rStar boosts GSM8K accuracy from 12.51% to 63.91% for LLaMA2-7B, from 36.46% to 81.88% for Mistral-7B, from 74.53% to 91.13% for LLaMA3-8B-Instruct.
If you find our work helpful, please consider citing it:
@misc{qi2024mutual,
title={Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers},
author={Zhenting Qi and Mingyuan Ma and Jiahang Xu and Li Lyna Zhang and Fan Yang and Mao Yang},
year={2024},
eprint={2408.06195},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
rStar has been recommended as a key technique in Awesome LLM Strawberry (OpenAI o1). Click and read more relevant papers.