Code for finetuning the Code Llama 7B and Llemma 7B models on the MetaMathQA dataset.

Instructions for replicating the finetuning experiments in Azerbayev et al. (2023) are below.

First, modify env_setup.sh to declare the BASE_DIR and TRAIN_FILE environment variables correctly. Then, from the base directory of this repository, run

./train_scipt/train_llama2_full.sh
./train_script/train_codellama_full.sh
./train_script/train_llemma_7b_full.sh

Note that the train_llama2_full.sh script is designed to replicate the experiments in Yu et al. (2023). The scripts are designed for an 8xA100 80GB configuration: modify them for your hardware as appropriate.

Once the models have finished finetuning, run

./eval_scripts/llama2_eval.sh
./eval_scripts/codellama_eval.sh
./eval_scripts/llemma_eval.sh

from the base directory of this repository to replicate evaluation results.

# Add Llemma citation

@misc{yu2023metamath,
      title={MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models}, 
      author={Longhui Yu and Weisen Jiang and Han Shi and Jincheng Yu and Zhengying Liu and Yu Zhang and James T. Kwok and Zhenguo Li and Adrian Weller and Weiyang Liu},
      year={2023},
      eprint={2309.12284},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}