library_name: transformers
license: other
license_name: nvidia-internal-scientific-research-and-development-model-license
license_link: >-
https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-internal-scientific-research-and-development-model-license/
pipeline_tag: text-generation
tags:
- nvidia
- pytorch
OpenCode-Nemotron-7B Overview
Description
OpenCode-Nemotron-7B is a large language model (LLM) which is a derivative of Qwen2.5-7B-Instruct (AKA the reference model). It is a reasoning model that is post trained for reasoning while code generation. The model supports a context length of 32K tokens.
This model is ready for commercial use.
License/Terms of Use
GOVERNING TERMS: Your use of this model is governed by the NVIDIA Internal Scientific Research and Development Model License.
Deployment Geography:
Global
Use Case:
This model is intended for developers and researchers building LLMs.
Release Date:
Huggingface [04/25/2025] via https://huggingface.co/nvidia/OpenCode-Nemotron-7B/
References
Model Architecture
- Architecture Type: Dense decoder-only Transformer model
- Network Architecture: Qwen2.5-32B-Instruct
Input
Input Type(s): Text
Input Format(s): String
Input Parameters: One-Dimensional (1D)
Other Properties Related to Input: Context length up to 32,768 tokens
Output
Output Type(s): Text
Output Format: String
Output Parameters: One-Dimensional (1D)
Other Properties Related to Output: Context length up to 32,768 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: Transformers, vLLM
- Recommended Hardware Microarchitecture Compatibility:
- NVIDIA Ampere
- NVIDIA Hopper
- Preferred/Supported Operating System(s): Linux
Model Version(s)
1.0 (4/25/2025)
Training Dataset
The training corpus for OpenCode-Nemotron-7B is OpenCodeReasoning dataset, which is composed of competitive programming questions and DeepSeek-R1 generated responses.
- Data Collection Method: Hybrid: Automated, Human, Synthetic
- Data Labeling Method: Hybrid: Automated, Human, Synthetic
Evaluation Dataset
We used the datasets listed in the next section to evaluate OpenCodeReasoning-32B.
- Data Collection Method: Hybrid: Automated, Human, Synthetic
- Data Labeling Method: Hybrid: Automated, Human, Synthetic
LiveCodeBench
| Easy | Medium | Hard | Avg. |
|---|---|---|---|
| 95.4 | 64.0 | 18.0 | 51.3 |
CodeContests
| Public | Private | Generated | All |
|---|---|---|---|
| 46.7 | 29.6 | 32.3 | 18.1 |
Inference
Engine: vLLM
Test Hardware NVIDIA H100-80GB
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.
Citation
If you find the data useful, please cite:
@article{ahmad2025opencodereasoning,
title={OpenCodeReasoning: Advancing Data Distillation for Competitive Coding},
author={Wasi Uddin Ahmad, Sean Narenthiran, Somshubra Majumdar, Aleksander Ficek, Siddhartha Jain, Jocelyn Huang, Vahid Noroozi, Boris Ginsburg},
year={2025},
eprint={2504.01943},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2504.01943},
}