The computing resources used in this tutorial are dual-card A6000.
OpenCodeReasoning-Nemotron-32B, released by NVIDIA on May 9, 2025, is a high-performance large language model designed specifically for code reasoning and generation. It is the flagship version of the OpenCodeReasoning (OCR) model suite, supporting a context length of 32K tags. Related research papers are available. OpenCodeReasoning: Advancing Data Distillation for Competitive Coding .
2. Project Examples
3. Operation steps
1. Start the container
If "Model" is not displayed, it means the model is being initialized. Since the model is large, please wait about 2-3 minutes and refresh the page.
2. After entering the webpage, you can start a conversation with the model
4. Discussion
🖌️ If you see a high-quality project, please leave a message in the background to recommend it! In addition, we have also established a tutorial exchange group. Welcome friends to scan the QR code and remark [SD Tutorial] to join the group to discuss various technical issues and share application effects↓
Citation Information
Thanks to Github user SuperYang Deployment of this tutorial. The reference information of this project is as follows:
@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},
}
Build AI with AI
From idea to launch — accelerate your AI development with free AI co-coding, out-of-the-box environment and best price of GPUs.
The computing resources used in this tutorial are dual-card A6000.
OpenCodeReasoning-Nemotron-32B, released by NVIDIA on May 9, 2025, is a high-performance large language model designed specifically for code reasoning and generation. It is the flagship version of the OpenCodeReasoning (OCR) model suite, supporting a context length of 32K tags. Related research papers are available. OpenCodeReasoning: Advancing Data Distillation for Competitive Coding .
2. Project Examples
3. Operation steps
1. Start the container
If "Model" is not displayed, it means the model is being initialized. Since the model is large, please wait about 2-3 minutes and refresh the page.
2. After entering the webpage, you can start a conversation with the model
4. Discussion
🖌️ If you see a high-quality project, please leave a message in the background to recommend it! In addition, we have also established a tutorial exchange group. Welcome friends to scan the QR code and remark [SD Tutorial] to join the group to discuss various technical issues and share application effects↓
Citation Information
Thanks to Github user SuperYang Deployment of this tutorial. The reference information of this project is as follows:
@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},
}
Build AI with AI
From idea to launch — accelerate your AI development with free AI co-coding, out-of-the-box environment and best price of GPUs.