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ToheartZhang_-_JiuZhang3.0-Synthesis-7B-gguf
RichardErkhov/ToheartZhang_-_JiuZhang3.0-Synthesis-7B-gguf
ToheartZhang_-_JiuZhang3.0-Synthesis-7B-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
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At a glance
- Access
- Public
- Created
- Sep 19, 2024
- Updated
- Sep 19, 2024
- SHA
- 775db066
Model card
From the Hugging Face model README
Quantization made by Richard Erkhov.
JiuZhang3.0-Synthesis-7B - GGUF
- Model creator: https://huggingface.co/ToheartZhang/
- Original model: https://huggingface.co/ToheartZhang/JiuZhang3.0-Synthesis-7B/
Original model description:
language:
- en
<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
| 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 | 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 | 88.6 | 52.8 | 90.4 | 92.6 | 97.3 | 51.0 | 78.8 |
| JiuZhang3.0-8B | 88.6 | 51.0 | 89.4 | 92.6 | 97.1 | 50.9 | 78.3 |
Data Format
This is a fine-tuned model for synthesizing math-related question-solution pairs. Please use the given prompts.
Syntheized Data Type
- Grade school
- Middel school
- High school
- College
- AMC 8
- AMC 10
- AMC 12
- AIME
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},
}
- Created
- Sep 19, 2024
- Updated
- Sep 19, 2024