---
language:
- en
datasets:
- ToheartZhang/JiuZhang3.0-Corpus-PT-CoT
- ToheartZhang/JiuZhang3.0-Corpus-PT-Tool
- ToheartZhang/JiuZhang3.0-Corpus-SFT
---
<h1 align="center">
JiuZhang3.0: Efficiently Improving Mathematical
Reasoning by Training Small Data Synthesis Models
</h1>
<p align="center">
  <a href="https://arxiv.org/abs/2405.14365"><b>[Paper]</b></a> •
  <a href="https://github.com/RUCAIBox/JiuZhang3.0"><b>[GitHub]</b></a> •
  <a href="https://huggingface.co/collections/ToheartZhang/jiuzhang30-66508be8be5a61de47101655#/"><b>[Models]</b></a> •
  <a href="https://huggingface.co/collections/ToheartZhang/jiuzhang30-corpus-665092209525389ad7a2289a"><b>[Data]</b></a>
</p>

## Introduction
JiuZhang3.0 is a series of fine-tuned models for math reasoning continually pre-trained on corpus synthesized by our carefully trained small LLM.

## Experimental Results
For more evaluation results, please refer to the [Paper](https://arxiv.org/abs/2405.14365)

| Models                   | GSM8k | MATH | SVAMP | ASDiv | MAWPS | CARP | Avg.  |
|--------------------------|-------|------|-------|-------|-------|------|-------|
| GPT-4                    | 92.2  | 65.4 | 92.9  | 94.3  | 96.6  | 53.6 | 82.5  |
|**20B+ Models**||
| Llemma-34B               | 60.2  | 24.6 | 68.0  | 75.6  | 89.8  | 36.5 | 59.1  |
| Intern-Math-20B          | 64.9  | 27.4 | 74.9  | 79.6  | 94.4  | 42.3 | 63.9  |
| ChatGLM-Math-32B         | 82.6  | 40.6 | -     | -     | -     | -    | -     |
| MAmmoTH2-8x7B-Plus       | _86.4_| 47.0 | _90.0_| _92.2_| **97.0** | 45.8 | _76.4_ |
| [JiuZhang3.0-8x7B](https://huggingface.co/ToheartZhang/JiuZhang3.0-8x7B)   | **89.8** | **53.8** | **90.2** | **93.1** | _96.7_ | 52.3 | **79.3** |
|**7-8B Models**||
| Mistral-7B-MMIQC         | 75.0  | 34.2 | 73.5  | 82.1  | 90.1  | 36.5 | 65.2  |
| MetaMath-Mistral-7B      | 77.8  | 29.6 | 79.6  | 81.2  | 93.7  | 30.5 | 65.4  |
| Abel-7B-002              | 80.4  | 29.6 | 78.8  | 82.7  | 93.5  | 33.2 | 66.4  |
| WizardMath-7B-1.1        | 82.2  | 32.8 | 80.7  | 84.2  | 93.8  | 31.9 | 67.6  |
| Math-Shepherd-Mistral-7B | 84.3  | 34.4 | 82.9  | 82.8  | 92.5  | 32.9 | 68.3  |
| KPMath-DSMath-7B         | 83.9  | 48.8 | 81.5  | 88.9  | 94.8  | -    | -     |
| MAmmoTH2-7B-Plus         | 84.2  | 46.2 | _90.3_| 90.3  | _97.1_| 44.3 | 75.2  |
| MAmmoTH2-8B-Plus         | 84.4  | 41.2 | 89.9  | 89.9  | _97.1_| 44.8 | 74.6  |
| DeepSeekMath-7B-Instruct | 82.3  | 45.8 | 83.7  | 90.1  | 95.7  | 45.8 | 73.9  |
| DeepSeekMath-7B-RL       | 88.2  | 50.2 | 87.3  | 91.8  | 95.5  | **51.6** | 77.4  |
| [JiuZhang3.0-7B](https://huggingface.co/ToheartZhang/JiuZhang3.0-7B)     | **88.6** | **52.8** | **90.4** | **92.6** | **97.3** | _51.0_ | **78.8** |
| [JiuZhang3.0-8B](https://huggingface.co/ToheartZhang/JiuZhang3.0-8B)    | **88.6** | _51.0_ | 89.4  | **92.6** | _97.1_ | 50.9 | _78.3_ |

## Evaluation
### Natural Language Reasoning
```
## Question
{question}

## Solution
{solution}
```

### Tool Manipulation
```
## Question
{question}

## Code Solution
{solution}
```

## Citation
If you find this repository helpful, please consider citing our paper:

```
@article{zhou2024jiuzhang30,
      title={JiuZhang3.0: Efficiently Improving Mathematical Reasoning by Training Small Data Synthesis Models}, 
      author={Kun Zhou and Beichen Zhang and Jiapeng Wang and Zhipeng Chen and Wayne Xin Zhao and Jing Sha and Zhichao Sheng and Shijin Wang and Ji-Rong Wen},
      year={2024},
}
```