Openmath Nemotron 14B Kaggle

🚀 OpenMath-Nemotron-14B-Kaggle

OpenMath-Nemotron-14B-Kaggle is a remarkable model created by fine-tuning Qwen/Qwen2.5-14B on a subset of the OpenMathReasoning dataset. This model played a crucial role in our first-place submission to the AIMO-2 Kaggle competition!

OpenMath-Nemotron models have achieved state-of-the-art results on popular mathematical benchmarks. We present metrics as pass@1 (maj@64), where pass@1 represents the average accuracy across 64 generations, and maj@64 is the result of majority voting. For more details on the evaluation setup, please refer to our paper.

🚀 Quick Start

Reproducing our results

The pipeline we used to produce the data and models is fully open-sourced!

• Code
• Models
• Dataset
• Paper

We provide all instructions to fully reproduce our results, including data generation.

How to use the models?

This model will always use code execution to solve math problems. Therefore, we highly recommend running inference with our reference implementation in NeMo-Skills.

⚠️ Important Note

These models have not been instruction tuned on general data and thus might not provide good answers outside of the math domain.

📄 License

GOVERNING TERMS: Use of this model is governed by CC-BY-4.0. Additional Information: Apache License Version 2.0.

📚 Documentation

Deployment Geography

Global

Use Case

This model is intended to facilitate research in the area of mathematical reasoning.

Release Date

Huggingface 04/23/2025

Model Architecture

• Architecture Type: Transformer decoder-only language model
• Network Architecture: Qwen2.5
• This model was developed based on Qwen2.5-1.5B
• This model has 1.5B of model parameters.

Input

• Input Type(s): Text
• Input Format(s): String
• Input Parameters: One-Dimensional (1D)
• Other Properties Related to Input: Context length up to 131,072 tokens

Output

• Output Type(s): Text
• Output Format: String
• Output Parameters: One-Dimensional (1D)
• Other Properties Related to Output: Context length up to 131,072 tokens

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g., GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.

Software Integration

• Runtime Engine(s):
  • Tensor RT / Triton
• Supported Hardware Microarchitecture Compatibility:
  • NVIDIA Ampere
  • NVIDIA Hopper
• Preferred Operating System(s):
  • Linux

Model Version(s)

• OpenMath-Nemotron-1.5B
• OpenMath-Nemotron-7B
• OpenMath-Nemotron-14B
• OpenMath-Nemotron-32B

Ethical Considerations

NVIDIA believes Trustworthy AI is a shared responsibility, and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets the requirements for the relevant industry and use case and addresses unforeseen product misuse.

For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards.

Please report security vulnerabilities or NVIDIA AI Concerns here.

Citation

If you find our work useful, please consider citing us!

@article{moshkov2025aimo2,
  title   = {AIMO-2 Winning Solution: Building State-of-the-Art Mathematical Reasoning Models with OpenMathReasoning dataset},
  author  = {Ivan Moshkov and Darragh Hanley and Ivan Sorokin and Shubham Toshniwal and Christof Henkel and Benedikt Schifferer and Wei Du and Igor Gitman},
  year    = {2025},
  journal = {arXiv preprint arXiv:2504.16891}
}