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}
}