Qwen2.5-Math-7B-CFT
| Property | Value |
|---|---|
| Parameter Count | 7 Billion |
| Training Data | WebInstruct-CFT-50K |
| Hardware Used | 8x NVIDIA H100 GPUs |
| Model URL | Hugging Face |
What is Qwen2.5-Math-7B-CFT?
Qwen2.5-Math-7B-CFT represents a breakthrough in mathematical reasoning AI models, introducing a novel Critique Fine-Tuning (CFT) approach. Unlike traditional models that learn through imitation, this model is trained to analyze and critique responses, leading to superior reasoning capabilities. Despite using only 50K training samples, it achieves remarkable performance metrics, including 79.4% accuracy on MATH and 41.6% on OlympiadBench benchmarks.
Implementation Details
The model utilizes the LLaMA-Factory framework and implements a unique training methodology where the input consists of queries paired with noisy responses, and the output is a critique. The training process leverages GPT-4 as a teacher model for generating critiques, completed in approximately one hour using DeepSpeed Zero-3 optimization.
- Novel critique-based training methodology
- Exceptional data efficiency (40x less data than comparable models)
- Built on Qwen2.5-Math-7B foundation
- Implements DeepSpeed Zero-3 for efficient training
Core Capabilities
- Advanced mathematical reasoning and problem-solving
- 4-10% performance improvement over traditional SFT approaches
- Efficient processing of complex mathematical queries
- Superior performance on standardized math benchmarks
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its Critique Fine-Tuning approach, which teaches it to analyze and critique responses rather than simply imitating correct answers. This results in deeper understanding and better reasoning capabilities while requiring significantly less training data.
Q: What are the recommended use cases?
The model excels in mathematical reasoning tasks, making it ideal for educational applications, mathematical problem-solving, and scenarios requiring advanced mathematical analysis. It's particularly effective for complex mathematical computations and proofs.
