OpenR1-Qwen-7B
| Property | Value |
|---|---|
| Parameter Count | 7 Billion |
| Base Model | Qwen2.5-Math-Instruct |
| Training Dataset | OpenR1-220k-Math |
| Model URL | HuggingFace |
What is OpenR1-Qwen-7B?
OpenR1-Qwen-7B is a specialized mathematical reasoning model that builds upon the Qwen2.5-Math-Instruct architecture. It has been fine-tuned on the OpenR1-220k-Math dataset to enhance its mathematical problem-solving capabilities. The model demonstrates impressive performance on various mathematical benchmarks, including MATH-500 and AIME tests.
Implementation Details
The model was trained for 3 epochs with specific optimizations: a learning rate of 5e-5, extended context length from 4k to 32k tokens, and increased RoPE frequency to 300k. The training process incorporated a linear learning rate schedule with a 10% warmup phase to ensure optimal performance.
- Extended context length capability (32k tokens)
- Optimized RoPE frequency (300k)
- Step-by-step mathematical reasoning
- Specialized boxed answer format
Core Capabilities
- Strong performance on MATH-500 (90.6% accuracy)
- Competitive results on AIME24 (36.7%) and AIME25 (40.0%)
- Structured mathematical problem-solving
- Clear step-by-step reasoning approach
Frequently Asked Questions
Q: What makes this model unique?
The model's specialized training on mathematical reasoning and its ability to provide structured, step-by-step solutions with boxed final answers makes it particularly effective for mathematical applications. Its extended context length and optimized training parameters set it apart from similar models.
Q: What are the recommended use cases?
The model is specifically designed for mathematical problem-solving, equation solving, and step-by-step mathematical reasoning. It's particularly well-suited for educational applications and complex mathematical computations that require detailed explanations.
