meta-math/MetaMath-Mistral-7B

see our paper in https://arxiv.org/abs/2309.12284

View the project page: https://meta-math.github.io/

Note

All MetaMathQA data are augmented from the training sets of GSM8K and MATH. None of the augmented data is from the testing set.

You can check the original_question in meta-math/MetaMathQA, each item is from the GSM8K or MATH train set.

Model Details

MetaMath-Mistral-7B is fully fine-tuned on the MetaMathQA datasets and based on the powerful Mistral-7B model. It is glad to see using MetaMathQA datasets and change the base model from llama-2-7B to Mistral-7b can boost the GSM8K performance from 66.5 to 77.7.

To fine-tune Mistral-7B, I would suggest using a smaller learning rate (usually 1/5 to 1/10 of the lr for LlaMa-2-7B) and staying other training args unchanged. More training details and scripts can be seen at https://github.com/meta-math/MetaMath

Installation

pip install transformers==4.35.0
pip install torch==2.0.1
pip install sentencepiece==0.1.99
pip install tokenizers==0.13.3
pip install accelerate==0.21.0
pip install bitsandbytes==0.40.0
pip install vllm
pip install fraction
pip install protobuf

Model Usage

prompting template:

'''

"Below is an instruction that describes a task. " "Write a response that appropriately completes the request.\n\n" "### Instruction:\n{instruction}\n\n### Response: Let's think step by step."

'''

where you need to use your query question to replace the {instruction}

There is another interesting repo about Arithmo-Mistral-7B in https://huggingface.co/akjindal53244/Arithmo-Mistral-7B, where they combine our MetaMathQA dataset and MathInstruct datasets to train a powerful model. Thanks agian for their contributions. We would also try to train the combination of MetaMathQA and MathInstruct datasets, and also open all the results and training details.

Experiments

Model	GSM8k Pass@1	MATH Pass@1
MPT-7B	6.8	3.0
Falcon-7B	6.8	2.3
LLaMA-1-7B	11.0	2.9
LLaMA-2-7B	14.6	2.5
MPT-30B	15.2	3.1
LLaMA-1-13B	17.8	3.9
GPT-Neo-2.7B	19.5	--
Falcon-40B	19.6	2.5
Baichuan-chat-13B	23.9	--
Vicuna-v1.3-13B	27.6	--
LLaMA-2-13B	28.7	3.9
InternLM-7B	31.2	--
ChatGLM-2-6B	32.4	--
GPT-J-6B	34.9	--
LLaMA-1-33B	35.6	3.9
LLaMA-2-34B	42.2	6.24
RFT-7B	50.3	--
LLaMA-1-65B	50.9	10.6
Qwen-7B	51.6	--
WizardMath-7B	54.9	10.7
LLaMA-2-70B	56.8	13.5
WizardMath-13B	63.9	14.0
MAmmoTH-7B (COT)	50.5	10.4
MAmmoTH-7B (POT+COT)	53.6	31.5
Arithmo-Mistral-7B	74.7	25.3
MetaMath-7B	66.5	19.8
MetaMath-13B	72.3	22.4
🔥 MetaMath-Mistral-7B	77.7	28.2

Citation

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

@article{jiang2023mistral,
  title={Mistral 7B},
  author={Jiang, Albert Q and Sablayrolles, Alexandre and Mensch, Arthur and Bamford, Chris and Chaplot, Devendra Singh and Casas, Diego de las and Bressand, Florian and Lengyel, Gianna and Lample, Guillaume and Saulnier, Lucile and others},
  journal={arXiv preprint arXiv:2310.06825},
  year={2023}
}