---
license: apache-2.0
datasets:
- meta-math/MetaMathQA
language:
- en
metrics:
- accuracy
---

see our paper in https://arxiv.org/abs/2401.02415

View the project page:
https://github.com/TencentARC/LLaMA-Pro


## Model Details

MetaMath-Mistral-Pro is fully fine-tuned on the MetaMathQA datasets and based on the powerful Mistral-Pro model.


## Model Usage

The model is trained to use the following format (note the newlines):
```
<|user|>
Your message here!
<|assistant|>
```

For best results, format all inputs in this manner. **Make sure to include a newline after `<|assistant|>`, this can affect generation quality quite a bit.**


## 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        |
|  MetaMath-Llemma-7B | 69.2     | 30.0        |
| 🔥 **MetaMath-Mistral-Pro** | **78.4**     | **30.3**        |


## Citation

```bibtex
@article{wu2024llama,
  title={Llama pro: Progressive llama with block expansion},
  author={Wu, Chengyue and Gan, Yukang and Ge, Yixiao and Lu, Zeyu and Wang, Jiahao and Feng, Ye and Luo, Ping and Shan, Ying},
  journal={arXiv preprint arXiv:2401.02415},
  year={2024}
}
```