| Property | Value |
|---|---|
| Model Size | 7B parameters |
| Developer | NVIDIA |
| License | CC Attribution Non-Commercial 4.0 |
| Base Model | Qwen2.5-Math-7B-Base |
| Hugging Face | nvidia/AceMath-7B-Instruct |
What is AceMath-7B-Instruct?
AceMath-7B-Instruct is part of NVIDIA's AceMath family of models specifically designed for mathematical reasoning. Built upon Qwen2.5-Math-7B-Base, this model excels at solving English mathematical problems using Chain-of-Thought (CoT) reasoning. It achieves an impressive 67.2% average pass@1 accuracy, outperforming previous state-of-the-art models of similar size and approaching the performance of models 10x larger.
Implementation Details
The model underwent a multi-stage supervised fine-tuning (SFT) process, starting with general-purpose data followed by math-specific SFT data. It's implemented using the transformers library and can be easily integrated into existing workflows.
- Specialized for mathematical problem-solving with CoT reasoning
- Multi-stage fine-tuning approach
- Outperforms Qwen2.5-Math-7B-Instruct (67.2 vs 62.9 pass@1)
- Compatible with Hugging Face transformers library
Core Capabilities
- Advanced mathematical reasoning and problem-solving
- Chain-of-Thought reasoning for step-by-step solutions
- English mathematical problem comprehension
- Competitive performance against larger models
Frequently Asked Questions
Q: What makes this model unique?
AceMath-7B-Instruct stands out for its exceptional mathematical reasoning capabilities despite its moderate size, achieving performance levels close to models 10x larger. It's specifically optimized for mathematical problem-solving using Chain-of-Thought reasoning.
Q: What are the recommended use cases?
The model is specifically recommended for solving mathematical problems. For other tasks like code or general knowledge, NVIDIA recommends using their AceInstruct series instead.