nvidia/OpenMath-Nemotron-32B

Hugging Face
TEXT GENERATIONConcurrency Cost:2Model Size:32.8BQuant:FP8Ctx Length:32kPublished:Apr 22, 2025License:cc-by-4.0Architecture:Transformer0.0K Open Weights Warm

The nvidia/OpenMath-Nemotron-32B is a 32.8 billion parameter, Qwen2.5-based transformer decoder-only language model developed by NVIDIA. Fine-tuned on the OpenMathReasoning dataset, it specializes in advanced mathematical reasoning tasks. This model achieves state-of-the-art results on popular mathematical benchmarks and supports a 131,072 token context length. It is designed for commercial use and research in mathematical problem-solving.

Loading preview...

OpenMath-Nemotron-32B Overview

OpenMath-Nemotron-32B is a 32.8 billion parameter model from NVIDIA, built upon the Qwen2.5 architecture. It is specifically fine-tuned using the OpenMathReasoning dataset to excel in complex mathematical reasoning. This model demonstrates state-of-the-art performance across various mathematical benchmarks, as detailed in its accompanying paper.

Key Capabilities

  • Advanced Mathematical Reasoning: Achieves high accuracy on benchmarks like AIME24, AIME25, HMMT-24-25, and HLE-Math, particularly when utilizing advanced inference modes.
  • Flexible Inference Modes: Supports Chain-of-Thought (CoT), Tool-Integrated Reasoning (TIR), and Generative Solution Selection (GenSelect) for diverse problem-solving approaches.
  • Long Context Window: Features a substantial context length of 131,072 tokens, enabling the processing of extensive mathematical problems and contexts.
  • Commercial Use Ready: Licensed under CC-BY-4.0 and Apache License Version 2.0, making it suitable for commercial applications.

Good For

  • Mathematical Problem Solving: Ideal for applications requiring precise and advanced mathematical reasoning.
  • Research in AI for Math: Provides a robust foundation for further research and development in automated mathematical problem-solving.
  • Competitive Math Challenges: A version of the 14B model secured first place in the AIMO-2 Kaggle competition, highlighting its capability in competitive environments.

It is important to note that this model is not instruction-tuned for general data and is optimized specifically for the mathematical domain.

Popular Sampler Settings

Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.

temperature
top_p
top_k
frequency_penalty
presence_penalty
repetition_penalty
min_p