OpenCodeReasoning-Nemotron-14B is a large language model (LLM) which is a derivative of Qwen2.5-14B-Instruct (AKA the reference model). It is a reasoning model that is post-trained for reasoning for code generation. The model supports a context length of 32K tokens.
This model is ready for commercial/non-commercial use.
1import transformers
2import torch
34model_id ="nvidia/OpenCodeReasoning-Nemotron-14B"56pipeline = transformers.pipeline(7"text-generation",8 model=model_id,9 model_kwargs={"torch_dtype": torch.bfloat16},10 device_map="auto",11)1213prompt ="""You are a helpful and harmless assistant. You should think step-by-step before responding to the instruction below.
1415Please use python programming language only.
1617You must use ```python for just the final solution code block with the following format:
18```python
19# Your code here
20```
2122{user}
23"""2425messages =[26{27"role":"user",28"content": prompt.format(user="Write a program to calculate the sum of the first $N$ fibonacci numbers")},29]3031outputs = pipeline(32 messages,33 max_new_tokens=32768,34)35print(outputs[0]["generated_text"][-1]['content'])36
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},
}
Additional Information
Model Architecture:
Architecture Type: Dense decoder-only Transformer model
Network Architecture: Qwen-14B-Instruct
This model was developed based on Qwen2.5-14B-Instruct and has 14B model parameters. OpenCodeReasoning-Nemotron-14B was developed based on Qwen2.5-14B-Instruct and has 14B 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 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.
The training corpus for OpenCodeReasoning-Nemotron-14B is OpenCodeReasoning dataset, which is composed of competitive programming questions and DeepSeek-R1 generated responses.
We used the datasets listed in the next section to evaluate OpenCodeReasoning-Nemotron-14B. Data Collection Method: Hybrid: Automated, Human, Synthetic Labeling Method: Hybrid: Automated, Human, Synthetic
License/Terms of Use:
GOVERNING TERMS: Use of this model is governed by Apache 2.0.
Deployment Geography:
Global
Use Case:
This model is intended for developers and researchers building LLMs.
[2504.01943] OpenCodeReasoning: Advancing Data Distillation for Competitive Coding
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.
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