OpenMath-Nemotron - NVIDIA's open-source mathematical inference model series
OpenMath-Nemotron is a series of open-source mathematical inference models from NVIDIA, specifically designed to solve complex mathematical problems, including Olympiad-level problems. The models are based on the large-scale dataset OpenMathReasoning...
What is OpenMath-Nemotron?
OpenMath-Nemotron is a series of open-source mathematical inference models from NVIDIA, specifically designed to solve complex mathematical problems, including Olympiad-level problems. The models are trained on the massive OpenMathReasoning dataset, which contains 540,000 unique problems and 3.2 million long inference solutions. The OpenMath-Nemotron series includes OpenMath-Nemotron-1.5B, OpenMath-Nemotron-7B, OpenMath-Nemotron-14B, and OpenMath-Nemotron-32B, as well as OpenMath-Nemotron-14B-Kaggle (the model used in the AIMO-2 competition). The 1.5B version outperforms the 14B DeepSeek-R1 model in certain tasks.
Main functions of OpenMath-Nemotron
- Solving complex mathematical problemsIt supports solving mathematical problems ranging from basic to Olympiad level.
- Long reasoning ability: Generate detailed problem-solving steps based on step-by-step thinking.
- Multimodal reasoningIt supports multiple reasoning methods and is suitable for different types of mathematical problems.
The technical principles of OpenMath-Nemotron
- Large-scale datasetsThe dataset was trained using OpenMathReasoning, a massive dataset containing 540,000 unique mathematical problems and 3.2 million long inference solutions. The data came from the Art of Problem Solving (AoPS) community forum and underwent rigorous screening and processing.
- Chain-of-Thought (CoT)The model is based on generating a series of intermediate problem-solving steps to progressively deduce a solution to the problem. It supports the model in thinking more deeply before generating the final answer.
- Tool-Integrated Reasoning (TIR)This approach integrates code execution with long inference based on iterative training, generation, and quality filtering. The model prompts the code for computation when needed, executes the code in a sandbox, and obtains a more accurate solution.
- Model training and optimizationThe Qwen2.5-Base model is trained using supervised fine-tuning (SFT) technology, supporting multiple tasks including CoT solution generation, TIR solution generation, and GenSelect. The AdamW optimizer and cosine learning rate decay strategy, combined with sequence packing and context parallelization techniques, significantly accelerate training on long inference data.
- Inference optimizationModel inference optimization is based on TensorRT-LLM, supporting dynamic batch processing and various quantization techniques such as int8 and FP8, improving inference speed and reducing latency.
OpenMath-Nemotron project address
- GitHub repository:https://github.com/NVIDIA/NeMo-Skills
- HuggingFace model library:https://huggingface.co/collections/nvidia/openmathreasoning
- arXiv technical paper:https://arxiv.org/pdf/2504.16891
Application scenarios of OpenMath-Nemotron
- Mathematics EducationIt helps students and teachers solve math problems and improve learning outcomes.
- Competition TrainingIt helps math competition participants practice and optimize their problem-solving strategies.
- academic researchSupports the exploration of complex mathematical problems and contributes to academic research.
- Industrial applicationsSolve mathematical problems in practical engineering and finance to improve efficiency.
- AI DevelopmentAs a foundational model, it drives the development of AI systems that require mathematical reasoning.