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--- |
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language: |
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- en |
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datasets: |
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- ToheartZhang/JiuZhang3.0-Corpus-PT-CoT |
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- ToheartZhang/JiuZhang3.0-Corpus-PT-Tool |
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- ToheartZhang/JiuZhang3.0-Corpus-SFT |
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--- |
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<h1 align="center"> |
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JiuZhang3.0: Efficiently Improving Mathematical |
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Reasoning by Training Small Data Synthesis Models |
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</h1> |
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<p align="center"> |
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<a href="https://arxiv.org/abs/2405.14365"><b>[Paper]</b></a> • |
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<a href="https://github.com/RUCAIBox/JiuZhang3.0"><b>[GitHub]</b></a> • |
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<a href="https://huggingface.co/collections/ToheartZhang/jiuzhang30-66508be8be5a61de47101655#/"><b>[Models]</b></a> • |
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<a href="https://huggingface.co/collections/ToheartZhang/jiuzhang30-corpus-665092209525389ad7a2289a"><b>[Data]</b></a> |
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</p> |
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|
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## Introduction |
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JiuZhang3.0 is a series of fine-tuned models for math reasoning continually pre-trained on corpus synthesized by our carefully trained small LLM. |
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## Experimental Results |
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For more evaluation results, please refer to the [Paper](https://arxiv.org/abs/2405.14365) |
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| Models | GSM8k | MATH | SVAMP | ASDiv | MAWPS | CARP | Avg. | |
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|--------------------------|-------|------|-------|-------|-------|------|-------| |
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| GPT-4 | 92.2 | 65.4 | 92.9 | 94.3 | 96.6 | 53.6 | 82.5 | |
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|**20B+ Models**|| |
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| Llemma-34B | 60.2 | 24.6 | 68.0 | 75.6 | 89.8 | 36.5 | 59.1 | |
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| Intern-Math-20B | 64.9 | 27.4 | 74.9 | 79.6 | 94.4 | 42.3 | 63.9 | |
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| ChatGLM-Math-32B | 82.6 | 40.6 | - | - | - | - | - | |
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| MAmmoTH2-8x7B-Plus | _86.4_| 47.0 | _90.0_| _92.2_| **97.0** | 45.8 | _76.4_ | |
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| [JiuZhang3.0-8x7B](https://huggingface.co/ToheartZhang/JiuZhang3.0-8x7B) | **89.8** | **53.8** | **90.2** | **93.1** | _96.7_ | 52.3 | **79.3** | |
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|**7-8B Models**|| |
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| Mistral-7B-MMIQC | 75.0 | 34.2 | 73.5 | 82.1 | 90.1 | 36.5 | 65.2 | |
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| MetaMath-Mistral-7B | 77.8 | 29.6 | 79.6 | 81.2 | 93.7 | 30.5 | 65.4 | |
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| Abel-7B-002 | 80.4 | 29.6 | 78.8 | 82.7 | 93.5 | 33.2 | 66.4 | |
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| WizardMath-7B-1.1 | 82.2 | 32.8 | 80.7 | 84.2 | 93.8 | 31.9 | 67.6 | |
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| Math-Shepherd-Mistral-7B | 84.3 | 34.4 | 82.9 | 82.8 | 92.5 | 32.9 | 68.3 | |
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| KPMath-DSMath-7B | 83.9 | 48.8 | 81.5 | 88.9 | 94.8 | - | - | |
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| MAmmoTH2-7B-Plus | 84.2 | 46.2 | _90.3_| 90.3 | _97.1_| 44.3 | 75.2 | |
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| MAmmoTH2-8B-Plus | 84.4 | 41.2 | 89.9 | 89.9 | _97.1_| 44.8 | 74.6 | |
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| DeepSeekMath-7B-Instruct | 82.3 | 45.8 | 83.7 | 90.1 | 95.7 | 45.8 | 73.9 | |
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| DeepSeekMath-7B-RL | 88.2 | 50.2 | 87.3 | 91.8 | 95.5 | **51.6** | 77.4 | |
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| [JiuZhang3.0-7B](https://huggingface.co/ToheartZhang/JiuZhang3.0-7B) | **88.6** | **52.8** | **90.4** | **92.6** | **97.3** | _51.0_ | **78.8** | |
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| [JiuZhang3.0-8B](https://huggingface.co/ToheartZhang/JiuZhang3.0-8B) | **88.6** | _51.0_ | 89.4 | **92.6** | _97.1_ | 50.9 | _78.3_ | |
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## Evaluation |
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### Natural Language Reasoning |
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``` |
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## Question |
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{question} |
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## Solution |
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{solution} |
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``` |
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### Tool Manipulation |
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``` |
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## Question |
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{question} |
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## Code Solution |
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{solution} |
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``` |
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## Citation |
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If you find this repository helpful, please consider citing our paper: |
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``` |
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@article{zhou2024jiuzhang30, |
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title={JiuZhang3.0: Efficiently Improving Mathematical Reasoning by Training Small Data Synthesis Models}, |
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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}, |
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year={2024}, |
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} |
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``` |
