| Property | Value |
|---|---|
| Parameter Count | 6.91B |
| License | Apache 2.0 |
| Base Model | deepseek-ai/deepseek-math-7b-base |
| Training Data | 860k+ math problem-solution pairs |
What is NuminaMath-7B-CoT?
NuminaMath-7B-CoT is a specialized language model designed specifically for mathematical problem-solving. It represents the first stage of a two-stage training process, focusing on chain-of-thought reasoning for complex mathematical problems. The model has been fine-tuned on a comprehensive dataset of over 860,000 mathematical problem-solution pairs, making it particularly effective for competition-level mathematics.
Implementation Details
The model was trained using a sophisticated approach with carefully selected hyperparameters, including a learning rate of 2e-05, a cosine learning schedule with 0.1 warmup ratio, and distributed training across 8 GPUs. The training process utilized the Adam optimizer and ran for 4 epochs with a total batch size of 32.
- Multi-GPU distributed training architecture
- Implemented using PyTorch 2.3.1 and Transformers 4.40.1
- Optimized with F32 tensor type for precise mathematical computations
Core Capabilities
- Solves problems at AMC 12 competition level
- Generates detailed chain-of-thought reasoning
- Handles complex mathematical concepts and problem-solving
- Provides structured, step-by-step solutions
Frequently Asked Questions
Q: What makes this model unique?
The model's specialization in mathematical reasoning and its training on a vast dataset of competition-level problems sets it apart. It's specifically designed to show its work through chain-of-thought reasoning, making it valuable for educational and analytical purposes.
Q: What are the recommended use cases?
The model is best suited for solving mathematical problems up to AMC 12 level, particularly when detailed solution steps are needed. However, it may struggle with higher-level olympiad problems and geometry questions requiring visual understanding.