tomasmcm / metamath-mistral-7b

Source: meta-math/MetaMath-Mistral-7B ✦ Quant: TheBloke/MetaMath-Mistral-7B-AWQ ✦ Bootstrap Your Own Mathematical Questions for Large Language Models

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  • 364 runs
  • GitHub
  • Paper
  • License

Input

Output

Run time and cost

This model runs on Nvidia A40 (Large) GPU hardware. Predictions typically complete within 2 seconds.

Readme

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

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

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 instal torch==2.0.1
pip instal sentencepiece==0.1.99
pip instal tokenizers==0.13.3
pip instal accelerate==0.21.0
pip instal bitsandbytes==0.40.0
pip instal vllm
pip instal 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}
}