Qwen2.5-Math-PRM-7B
| Property | Value |
|---|---|
| Model Size | 7B parameters |
| Author | Qwen |
| Framework | Hugging Face Transformers |
| Paper | arXiv:2501.07301 |
| Requirements | transformers>=4.40.0 |
What is Qwen2.5-Math-PRM-7B?
Qwen2.5-Math-PRM-7B is a specialized Process Reward Model designed to evaluate and supervise mathematical reasoning steps. Unlike traditional language models, it focuses on identifying and assessing the quality of intermediate reasoning steps, providing numerical rewards between 0 and 1 for each step in a mathematical solution.
Implementation Details
The model operates by processing mathematical solutions where steps are separated by double line breaks. It utilizes special tokens ("
- Step-by-step evaluation capability
- Probability-based reward computation
- Integration with Hugging Face Transformers
- Support for batch processing
Core Capabilities
- Process supervision in mathematical reasoning
- Error identification in intermediate steps
- Best-of-N (BoN) evaluation support
- Strong performance in ProcessBench
- Compatible with Qwen2.5-Math-Instruct outputs
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized focus on evaluating mathematical reasoning processes rather than generating solutions. It can identify potential errors and assess the quality of each step in a mathematical solution, making it valuable for educational and verification purposes.
Q: What are the recommended use cases?
The model is ideal for evaluating mathematical solutions, providing feedback on reasoning steps, and helping identify where potential errors might occur in mathematical problem-solving processes. It's particularly useful in educational contexts and for validating mathematical reasoning chains.
