| license: mit | |
| library_name: transformers | |
| pipeline_tag: question-answering | |
| ```markdown | |
| <div align="center"> | |
| <h1> | |
| <b>m1</b>: Unleash the Potential of Test-Time Scaling for Medical Reasoning in Large Language Models | |
| </h1> | |
| <p> | |
| A simple test-time scaling strategy, with minimal fine-tuning, can unlock strong medical reasoning within large language models. | |
| </p> | |
| </div> | |
| This repository contains the model presented in the paper [m1: Unleash the Potential of Test-Time Scaling for Medical Reasoning in Large Language Models](https://huggingface.co/papers/2504.00869). | |
| Code: https://github.com/UCSC-VLAA/m1 | |
| ## ⚡ Introduction | |
| Hi! Welcome to the huggingface repository for m1! | |
| **m1** is a medical LLM designed to enhance reasoning through efficient test-time scaling. It enables lightweight models to match or exceed the performance of much larger counterparts by extending inference-time “thinking.” Unlike methods that rely on complex RL or expert supervision, m1 achieves strong results through: | |
| - **Fine-tuning on a small, high-quality set of verified medical reasoning examples**, showing that even with just 1K–23K examples, m1-7B *surpasses* models like HuatuoGPT-o1-7B and UltraMedical-8B, and m1-32B *rivals* 70B-scale models. | |
| - **Scaling reasoning at inference using token budgets**, which consistently improves performance across medical QA tasks—up to an optimal ~4K token budget, beyond which performance may degrade due to overthinking. | |
| - **Identifying medical knowledge as the key bottleneck**, revealing that additional reasoning alone cannot overcome knowledge gaps; instead, improvements require better data quality and increased model capacity. | |
| ``` |