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--- |
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license: llama2 |
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datasets: |
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- meta-math/MetaMathQA |
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--- |
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see our paper in https://arxiv.org/abs/2309.12284 |
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View the project page: |
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https://meta-math.github.io/ |
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## Note |
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All MetaMathQA data are augmented from the training sets of GSM8K and MATH. |
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<span style="color:red"><b>None of the augmented data is from the testing set.</b></span> |
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You can check the `original_question` in `meta-math/MetaMathQA`, each item is from the GSM8K or MATH train set. |
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## Model Details |
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MetaMath-Llemma-7B is fully fine-tuned on the MetaMathQA datasets and based on the powerful Llemma-7B model. It is glad to see using MetaMathQA datasets and change the base model from llama-2-7B to Llemma-7B can boost the MATH performance from 19.8 to **30.0**. |
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## Installation |
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``` |
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pip install transformers==4.35.0 |
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pip install torch==2.0.1 |
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pip install sentencepiece==0.1.99 |
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pip install tokenizers==0.13.3 |
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pip install accelerate==0.21.0 |
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pip install bitsandbytes==0.40.0 |
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pip install vllm |
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pip install fraction |
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pip install protobuf |
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``` |
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## Model Usage |
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prompting template: |
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''' |
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"Below is an instruction that describes a task. " |
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"Write a response that appropriately completes the request.\n\n" |
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"### Instruction:\n{instruction}\n\n### Response: Let's think step by step." |
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''' |
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where you need to use your query question to replace the {instruction} |
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## Experiments |
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| Model | GSM8k Pass@1 | MATH Pass@1 | |
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|---------------------|--------------|-------------| |
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| MPT-7B | 6.8 | 3.0 | |
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| Falcon-7B | 6.8 | 2.3 | |
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| LLaMA-1-7B | 11.0 | 2.9 | |
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| LLaMA-2-7B | 14.6 | 2.5 | |
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| MPT-30B | 15.2 | 3.1 | |
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| LLaMA-1-13B | 17.8 | 3.9 | |
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| GPT-Neo-2.7B | 19.5 | -- | |
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| Falcon-40B | 19.6 | 2.5 | |
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| Baichuan-chat-13B | 23.9 | -- | |
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| Vicuna-v1.3-13B | 27.6 | -- | |
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| LLaMA-2-13B | 28.7 | 3.9 | |
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| InternLM-7B | 31.2 | -- | |
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| ChatGLM-2-6B | 32.4 | -- | |
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| GPT-J-6B | 34.9 | -- | |
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| LLaMA-1-33B | 35.6 | 3.9 | |
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| LLaMA-2-34B | 42.2 | 6.24 | |
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| RFT-7B | 50.3 | -- | |
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| LLaMA-1-65B | 50.9 | 10.6 | |
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| Qwen-7B | 51.6 | -- | |
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| WizardMath-7B | 54.9 | 10.7 | |
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| LLaMA-2-70B | 56.8 | 13.5 | |
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| WizardMath-13B | 63.9 | 14.0 | |
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| MAmmoTH-7B (COT) | 50.5 | 10.4 | |
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| MAmmoTH-7B (POT+COT)| 53.6 | 31.5 | |
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| Arithmo-Mistral-7B | 74.7 | 25.3 | |
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| MetaMath-7B | 66.5 | 19.8 | |
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| MetaMath-13B | 72.3 | 22.4 | |
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| 🔥 **MetaMath-Llemma-7B** | **69.2** | **30.0** | |
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| 🔥 **MetaMath-Mistral-7B** | **77.7** | **28.2** | |
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## Citation |
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```bibtex |
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@article{yu2023metamath, |
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title={MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models}, |
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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}, |
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journal={arXiv preprint arXiv:2309.12284}, |
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year={2023} |
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} |
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``` |
