Opencodereasoning Nemotron 7B

🚀 OpenCodeReasoning-Nemotron-7B

OpenCodeReasoning-Nemotron-7B is a large language model derived from Qwen2.5-7B-Instruct, post - trained for code generation reasoning, supporting a 32K token context length and suitable for both commercial and non - commercial use.

🚀 Quick Start

To run inference on coding problems, you can use the following code:

import transformers
import torch

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

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

prompt = """You are a helpful and harmless assistant. You should think step-by-step before responding to the instruction below.

Please use python programming language only.

You must use ```python for just the final solution code block with the following format:
```python
# Your code here

{user} """

messages = [ { "role": "user", "content": prompt.format(user="Write a program to calculate the sum of the first $N$ fibonacci numbers")}, ]

outputs = pipeline( messages, max_new_tokens=32768, ) print(outputs[0]["generated_text"][-1]['content'])

## ✨ Features
- **Based on Qwen2.5-7B-Instruct**: OpenCodeReasoning-Nemotron-7B is a derivative of Qwen2.5-7B-Instruct, a large language model.
- **Code Generation Reasoning**: It is a reasoning model post - trained for code generation, with a context length support of 32K tokens.
- **Commercial and Non - commercial Use**: The model is ready for both commercial and non - commercial use.

## 📦 Installation
The README does not provide specific installation steps, so this section is skipped.

## 💻 Usage Examples
### Basic Usage
```python
import transformers
import torch

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

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

prompt = """You are a helpful and harmless assistant. You should think step-by-step before responding to the instruction below.

Please use python programming language only.

You must use ```python for just the final solution code block with the following format:
```python
# Your code here

{user} """

messages = [ { "role": "user", "content": prompt.format(user="Write a program to calculate the sum of the first $N$ fibonacci numbers")}, ]

outputs = pipeline( messages, max_new_tokens=32768, ) print(outputs[0]["generated_text"][-1]['content'])

## 📚 Documentation
### Results from [OpenCodeReasoning](https://arxiv.org/abs/2504.01943)
Below results are the average of **64 evaluations** on each benchmark.

| Model                  | LiveCodeBench Avg. | CodeContest All |
|------------------------|--------------------|-----------------|
| DeepSeek - R1            | 65.6               | 26.2            |
| QwQ - 32B                | 61.3               | 20.2            |
|                        |                    |                 |
| **Distilled 7B+ Models** |                    |                 |
|                        |                    |                 |
| Bespoke - Stratos - 7B     | 14.7               | 2.0             |
| OpenThinker - 7B         | 25.5               | 5.0             |
| R1 - Distill - Qwen - 7B     | 38.0               | 11.1            |
| OlympicCoder - 7B        | 40.9               | 10.6            |
| **OCR - Qwen - 7B** | **48.5** | **16.3** |
| **OCR - Qwen - 7B - Instruct** | **51.3** | **18.1** |
|                        |                    |                 |
| **Distilled 14B+ Models**|                    |                 |
|                        |                    |                 |
| R1 - Distill - Qwen - 14B    | 51.3               | 17.6            |
| **OCR - Qwen - 14B** | **57.7** | **22.6** |
| **OCR - Qwen - 14B - Instruct**| **59.4** | **23.6** |
|                        |                    |                 |
| **Distilled 32B+ Models**|                    |                 |
|                        |                    |                 |
| Bespoke - Stratos - 32B    | 30.1               | 6.3             |
| OpenThinker - 32B        | 54.1               | 16.4            |
| R1 - Distill - Qwen - 32B    | 58.1               | 18.3            |
| OlympicCoder - 32B       | 57.4               | 18.0            |
| **OCR - Qwen - 32B** | **61.8** | **24.6** |
| **OCR - Qwen - 32B - Instruct**| **61.7** | **24.4** |

### Reproducing our results
- [Models](https://huggingface.co/collections/nvidia/opencodereasoning-2-68168f37cd7c6beb1e3f92e7)
- [Dataset](https://huggingface.co/datasets/nvidia/OpenCodeReasoning)
- [Paper](https://arxiv.org/abs/2504.01943)

### Additional Information
#### Model Architecture
| Property | Details |
|----------|---------|
| Model Type | Dense decoder - only Transformer model |
| Network Architecture | Qwen - 7B - Instruct |
| Model Parameters | 7B |

#### Input
| Property | Details |
|----------|---------|
| 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
| Property | Details |
|----------|---------|
| Output Type(s) | Text |
| Output Format | String |
| Output Parameters | One - Dimensional (1D) |
| Other Properties Related to Output | Context length up to 32,768 tokens |

#### Software Integration
- Runtime Engine: NeMo 2.3.0
- Recommended Hardware Microarchitecture Compatibility: NVIDIA Ampere, NVIDIA Hopper
- Preferred/Supported Operating System(s): Linux

#### Model Version(s)
1.0 (4/25/2025)
OpenCodeReasoning - Nemotron - 7B
OpenCodeReasoning - Nemotron - 14B
OpenCodeReasoning - Nemotron - 32B
OpenCodeReasoning - Nemotron - 32B - IOI

### Training and Evaluation Datasets
#### Training Dataset
The training corpus for OpenCodeReasoning - Nemotron - 7B is [OpenCodeReasoning](https://huggingface.co/datasets/nvidia/OpenCodeReasoning) dataset, which is composed of competitive programming questions and DeepSeek - R1 generated responses.
- Data Collection Method: Hybrid: Automated, Human, Synthetic
- Labeling Method: Hybrid: Automated, Human, Synthetic
- Properties: 736k samples from OpenCodeReasoning (https://huggingface.co/datasets/nvidia/OpenCodeReasoning)

#### Evaluation Dataset
We used the datasets listed in the next section to evaluate OpenCodeReasoning - Nemotron - 7B.
- 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](https://huggingface.co/nvidia/OpenCode - Nemotron - 2 - 7B/blob/main/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/OpenCodeReasoning - Nemotron - 7B/

### Reference(s)
[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. Please report security vulnerabilities or NVIDIA AI Concerns here.

## 🔧 Technical Details
The README does not provide specific technical details, so this section is skipped.

## 📄 License
Use of this model is governed by [Apache 2.0](https://huggingface.co/nvidia/OpenCode - Nemotron - 2 - 7B/blob/main/LICENSE).

## 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}, }