NuminaMath-7B-TIR
| Property | Value |
|---|---|
| Parameter Count | 6.91B |
| Base Model | deepseek-ai/deepseek-math-7b-base |
| License | Apache 2.0 |
| Paper | Research Paper |
What is NuminaMath-7B-TIR?
NuminaMath-7B-TIR is an advanced language model specifically designed for mathematical problem-solving. It represents a significant achievement in AI-powered mathematics, having won the first progress prize in the AI Math Olympiad (AIMO) with a score of 29/50 on public and private test sets. The model employs a two-stage fine-tuning approach, combining traditional mathematical reasoning with tool-integrated reasoning capabilities.
Implementation Details
The model is implemented using a sophisticated two-stage training process: Stage 1 involves fine-tuning on diverse mathematical problems with Chain of Thought (CoT) reasoning, while Stage 2 focuses on tool-integrated reasoning using Python REPL for computational support. The model demonstrates exceptional performance across various benchmarks, including GSM8k (84.6%), MATH (68.1%), and AMC 2023 (20/40 questions).
- Two-stage supervised fine-tuning approach
- Integration with Python REPL for computational tasks
- BF16 tensor type optimization
- Supports both natural language reasoning and programmatic problem-solving
Core Capabilities
- Solves complex mathematical problems using tool-integrated reasoning
- Excels in competition-level mathematics (AMC 12 level)
- Generates step-by-step solutions with Python code integration
- Handles diverse mathematical concepts and problem types
Frequently Asked Questions
Q: What makes this model unique?
The model's unique strength lies in its ability to combine natural language reasoning with programmatic problem-solving, achieved through its innovative two-stage fine-tuning process and tool-integrated reasoning capabilities.
Q: What are the recommended use cases?
The model is specifically designed for solving mathematical problems, particularly those requiring complex reasoning and computation. It's ideal for educational purposes, competition-level mathematics, and automated mathematical problem-solving applications.
