SuperCorrect-7B
| Property | Value |
|---|---|
| Parameter Count | 7.62B |
| License | Apache 2.0 |
| Base Model | Qwen2.5-Math-7B-Instruct |
| Paper | arXiv:2410.09008 |
| Tensor Type | BF16 |
What is SuperCorrect-7B?
SuperCorrect-7B is a state-of-the-art language model specifically designed for mathematical reasoning. Developed through a novel two-stage fine-tuning method, it significantly outperforms existing models, showing a 7.8%/5.3% improvement over DeepSeekMath-7B and 15.1%/6.3% over Qwen2.5-Math-7B on MATH/GSM8K benchmarks.
Implementation Details
The model implements a unique hierarchical thought template called Buffer of Thought (BoT), enabling more deliberate reasoning compared to conventional Chain-of-Thought approaches. It requires transformers >= 4.37.0 and utilizes a structured XML format for step-by-step problem solving.
- Incorporates pre-defined hierarchical thought templates
- Implements error-driven insights for self-correction
- Uses XML-based formatting for clear step organization
- Supports detailed explanations with key annotations
Core Capabilities
- Advanced mathematical reasoning and problem-solving
- Self-correction and error analysis
- Structured thought process presentation
- Step-by-step solution generation with explanations
- Generalization of problem-solving strategies
Frequently Asked Questions
Q: What makes this model unique?
SuperCorrect-7B stands out through its innovative two-stage fine-tuning method and the incorporation of Buffer of Thought (BoT) reasoning framework, enabling superior performance in mathematical tasks without relying on external programming methods.
Q: What are the recommended use cases?
The model is particularly suited for mathematical problem-solving, educational applications, and scenarios requiring detailed step-by-step reasoning with self-correction capabilities.
