Qwen2.5-Math-7B-PRM800K
| Property | Value |
|---|---|
| Base Model | Qwen2.5-Math-7B-Instruct |
| Training Data | PRM800K Dataset |
| Purpose | Process Reward Model |
| Paper | ProcessBench Paper |
| Requirements | transformers>=4.40.0 |
What is Qwen2.5-Math-7B-PRM800K?
Qwen2.5-Math-7B-PRM800K is a specialized process reward model designed to evaluate the quality of mathematical reasoning steps. It serves as the baseline model in ProcessBench and has been fine-tuned on the PRM800K dataset, with careful consideration to avoid test data contamination from the MATH test set.
Implementation Details
The model operates by analyzing solution steps and providing feedback scores between 0 and 1, indicating the quality of reasoning. It requires steps to be separated by double line breaks and uses a special token "<extra_0>" for reward computation.
- Processes mathematical reasoning steps individually
- Outputs probability scores for step quality
- Implements advanced token masking for accurate reward calculation
- Compatible with Hugging Face Transformers library
Core Capabilities
- Step-by-step evaluation of mathematical solutions
- Quality assessment of intermediate reasoning steps
- Integration with transformer-based architectures
- Efficient processing with bfloat16 support
Frequently Asked Questions
Q: What makes this model unique?
This model specifically focuses on evaluating the process of mathematical reasoning rather than generating solutions. It provides granular feedback on each step's quality, making it valuable for assessing mathematical problem-solving approaches.
Q: What are the recommended use cases?
The model is best suited for evaluating mathematical solutions where step-by-step reasoning is important. It can be used to assess student work, validate automated mathematical solutions, or improve mathematical reasoning systems.
