metadata
license: apache-2.0
datasets:
- dyyyyyyyy/ScaleQuest-Math
language:
- en
metrics:
- accuracy
library_name: transformers
pipeline_tag: text-generation
Unleashing Reasoning Capability of LLMs via Scalable Question Synthesis from Scratch
Model Card for Qwen2-Math-7B-ScaleQuest
We introduce ScaleQuest, a scalable and novel data synthesis method that utilizes small-size open-source models to generate questions from scratch without the need for seed data with complex augmentation constraints.
- 📑 Project Page: https://scalequest.github.io
- 💻 Code: https://github.com/yyDing1/ScaleQuest
- 📖 Paper: Unleashing Reasoning Capability of LLMs via Scalable Question Synthesis from Scratch
- 💾 Models in the 🤗 HuggingFace Hub: ScaleQuest-Models
Datasets & Models
Math Dataset: link
We release two question generator models and four problem-solving models.
| Model | Type | MATH | Olympiad Bench | 🤗 HuggingFace Download Link |
|---|---|---|---|---|
| ScaleQuest-DeepSeekMath-7B-QGen | question generator | - | - | link |
| ScaleQuest-Qwen2-Math-7B-QGen | question generator | - | - | link |
| Mistral-7B-ScaleQuest | problem solver | 62.9 | 26.8 | link |
| Llama3-8B-ScaleQuest | problem solver | 64.4 | 25.3 | link |
| DeepSeekMath-7B-ScaleQuest | problem solver | 66.6 | 29.9 | link |
| Qwen2-Math-7B-ScaleQuest | problem solver | 73.4 | 38.5 | link |
Demo usage
Below is an example using Qwen2-Math-7B-ScaleQuest
Citation
@article{ding2024unleashing,
title={Unleashing Reasoning Capability of LLMs via Scalable Question Synthesis from Scratch},
author={Ding, Yuyang and Shi, Xinyu and Liang, Xiaobo and Li, Juntao and Zhu, Qiaoming and Zhang, Min},
journal={https://arxiv.org/abs/2410.18693},
year={2024}
}