Openmath Nemotron 7B

🚀 OpenMath-Nemotron-7B

OpenMath-Nemotron-7B is created by finetuning Qwen/Qwen2.5-Math-7B on OpenMathReasoning dataset. This model is ready for commercial use.

🚀 Quick Start

OpenMath-Nemotron models achieve state-of-the-art results on popular mathematical benchmarks. We present metrics as pass@1 (maj@64) where pass@1 is an average accuracy across 64 generations and maj@64 is the result of majority voting. Please see our paper for more details on the evaluation setup.

We used a version of OpenMath-Nemotron-14B model to secure the first place in AIMO-2 Kaggle competition!

✨ Features

• 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.
• Our models can be used in 3 inference modes: chain-of-thought (CoT), tool-integrated reasoning (TIR) and generative solution selection (GenSelect).

💻 Usage Examples

Basic Usage

import transformers
import torch

model_id = "nvidia/OpenMath-Nemotron-7B"

pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device_map="auto",
)

messages = [
    {
        "role": "user", 
        "content": "Solve the following math problem. Make sure to put the answer (and only answer) inside \\boxed{}.\n\n" + 
        "What is the minimum value of $a^2+6a-7$?"},
]

outputs = pipeline(
    messages,
    max_new_tokens=4096,
)
print(outputs[0]["generated_text"][-1]['content'])

Advanced Usage

To run inference with TIR or GenSelect modes, we highly recommend to use 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 math domain.

📚 Documentation

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}
}

Additional information

Information Table

Ethical Considerations

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