MMR1-Math-v0-7B
| Property | Value |
|---|---|
| Model Size | 7B parameters |
| Training Data | 6,000 samples |
| Training Infrastructure | 64 H100s, 6 hours |
| GitHub Repository | MMR1 Repository |
What is MMR1-Math-v0-7B?
MMR1-Math-v0-7B represents a breakthrough in multimodal mathematical reasoning, achieving state-of-the-art performance among open-source 7B models. The model demonstrates exceptional capabilities in processing and solving mathematical problems presented in both visual and textual formats, competing effectively with much larger proprietary models like GPT-4 and Gemini-2.0.
Implementation Details
The model utilizes an efficient training approach with GRPO (Guided Reinforcement Policy Optimization), completing training in just 6 hours using 64 H100 GPUs across 15 epochs. What makes this implementation particularly remarkable is its ability to achieve superior performance with only 6,000 carefully curated training samples.
- Balanced data strategy with uniform sampling across difficulty levels
- Integration with public datasets, thoroughly filtered for quality
- Efficient implementation using flash attention 2
- Compatible with the Transformers library for easy deployment
Core Capabilities
- Superior performance on MathVista (71.0%), significantly outperforming other 7B models
- Strong results on MathVision (30.2%), LogicVista (50.8%), and MathVerse (45.1%)
- Effective handling of multimodal mathematical reasoning tasks
- Balanced performance across varying difficulty levels
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to achieve SOTA performance with minimal training data (6k samples) sets it apart, demonstrating exceptional efficiency in learning and generalization. Its performance rivals that of much larger proprietary models while maintaining an open-source nature.
Q: What are the recommended use cases?
The model excels in mathematical reasoning tasks involving both visual and textual inputs, making it ideal for educational applications, automated math problem solving, and mathematical content analysis. It's particularly effective for complex mathematical reasoning scenarios requiring multimodal understanding.
