Hugging Face OpenMath-Nemotron-7B: Advanced Mathematical Reasoning
NVIDIA's OpenMath-Nemotron-7B is a 7.6 billion parameter language model built upon the Qwen2.5 architecture, specifically fine-tuned from Qwen/Qwen2.5-Math-7B. Its primary focus is on mathematical reasoning, leveraging the proprietary OpenMathReasoning dataset for training. This model has demonstrated state-of-the-art performance on challenging mathematical benchmarks, including AIME24, AIME25, HMMT-24-25, and HLE-Math, with notable pass@1 and majority voting (maj@64) scores.
Key Capabilities
- Specialized Mathematical Reasoning: Excels at solving complex math problems, as evidenced by its benchmark results and its role in securing first place in the AIMO-2 Kaggle competition.
- Multiple Inference Modes: Supports Chain-of-Thought (CoT), Tool-Integrated Reasoning (TIR), and Generative Solution Selection (GenSelect) for flexible problem-solving approaches.
- Extended Context Length: Features a substantial context window of up to 131,072 tokens, allowing for processing lengthy mathematical problems and derivations.
- Commercial Use Ready: The model is released under a CC-BY-4.0 license, making it suitable for commercial applications.
Good For
- Mathematical Research: Ideal for researchers exploring advanced mathematical problem-solving and reasoning with large language models.
- Competitive Math: Demonstrates strong performance in competitive math challenges, making it a valuable tool for similar applications.
- Developing Math-Centric AI: Provides a robust foundation for building applications that require precise and deep mathematical understanding.
It's important to note that while highly proficient in mathematics, this model has not been instruction-tuned on general data and may not perform optimally outside the mathematical domain. The full pipeline for data generation and model reproduction is open-sourced via NVIDIA/NeMo-Skills and detailed in their paper.