OpenMathReasoning
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OpenMath-Nemotron-14B-Kaggle
OpenMath-Nemotron-14B-Kaggle is created by finetuning Qwen/Qwen2.5-14B on a subset of OpenMathReasoning dataset. This model was used in our first place submission to the AIMO-2 Kaggle competition!
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.
| Model | AIME24 | AIME25 | HMMT-24-25 | HLE-Math |
|---|---|---|---|---|
| DeepSeek-R1-Distill-Qwen-1.5B | 26.8 (60.0) | 21.4 (36.7) | 14.2 (26.5) | 2.9 (5.0) |
| OpenMath-Nemotron-1.5B CoT | 61.6 (80.0) | 49.5 (66.7) | 39.9 (53.6) | 5.4 (5.4) |
| OpenMath-Nemotron-1.5B TIR | 52.0 (83.3) | 39.7 (70.0) | 37.2 (60.7) | 2.5 (6.2) |
| + Self GenSelect | 83.3 | 70.0 | 62.2 | 7.9 |
| + 32B GenSelect | 83.3 | 70.0 | 62.8 | 8.3 |
| DeepSeek-R1-Distill-Qwen-7B | 54.4 (80.0) | 38.6 (53.3) | 30.6 (42.9) | 3.3 (5.2) |
| OpenMath-Nemotron-7B CoT | 74.8 (80.0) | 61.2 (76.7) | 49.7 (57.7) | 6.6 (6.6) |
| OpenMath-Nemotron-7B TIR | 72.9 (83.3) | 57.5 (76.7) | 54.6 (66.3) | 7.8 (10.8) |
| + Self GenSelect | 86.7 | 76.7 | 68.4 | 11.5 |
| + 32B GenSelect | 86.7 | 76.7 | 69.9 | 11.9 |
| DeepSeek-R1-Distill-Qwen-14B | 65.8 (80.0) | 48.4 (60.0) | 40.1 (52.0) | 4.2 (4.8) |
| OpenMath-Nemotron-14B-MIX (kaggle) | 73.7 (86.7) | 57.9 (73.3) | 50.5 (64.8) | 5.7 (6.5) |
| OpenMath-Nemotron-14B CoT | 76.3 (83.3) | 63.0 (76.7) | 52.1 (60.7) | 7.5 (7.6) |
| OpenMath-Nemotron-14B TIR | 76.3 (86.7) | 61.3 (76.7) | 58.6 (70.9) | 9.5 (11.5) |
| + Self GenSelect | 86.7 | 76.7 | 72.4 | 14.1 |
| + 32B GenSelect | 90.0 | 76.7 | 71.9 | 13.7 |
| QwQ-32B | 78.1 (86.7) | 66.5 (76.7) | 55.9 (63.3) | 9.0 (9.5) |
| DeepSeek-R1-Distill-Qwen-32B | 66.9 (83.3) | 51.8 (73.3) | 39.9 (51.0) | 4.8 (6.0) |
| OpenMath-Nemotron-32B CoT | 76.5 (86.7) | 62.5 (73.3) | 53.0 (59.2) | 8.3 (8.3) |
| OpenMath-Nemotron-32B TIR | 78.4 (93.3) | 64.2 (76.7) | 59.7 (70.9) | 9.2 (12.5) |
| + Self GenSelect | 93.3 | 80.0 | 73.5 | 15.7 |
| DeepSeek-R1 | 79.1 (86.7) | 64.3 (73.3) | 53.0 (59.2) | 10.5 (11.4) |
Reproducing our results
The pipeline we used to produce the data and models is fully open-sourced!
We provide all instructions to fully reproduce our results, including data generation.
How to use the models?
Our models can be used in 3 inference modes: chain-of-thought (CoT), tool-integrated reasoning (TIR) and generative solution selection (GenSelect).
To run inference with CoT mode, you can use this example code snippet.
import transformers
import torch
model_id = "nvidia/OpenMath-Nemotron-14B-Kaggle"
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'])
To run inference with TIR or GenSelect modes, we highly recommend to use our reference implementation in NeMo-Skills.
Please note that these models have not been instruction tuned on general data and thus might not provide good answers outside of math domain.
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
License/Terms of Use:
GOVERNING TERMS: Use of this model is governed by CC-BY-4.0. Additional Information: Apache License Version 2.0.
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):
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 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.
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