Update README.md

README.md

@@ -1,121 +1,199 @@
 ---
 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/
+license_name: apache-2.0
+license_link: https://huggingface.co/nvidia/OpenCodeReasoning-Nemotron-14B/blob/main/LICENSE
 pipeline_tag: text-generation
 tags:
-  - nvidia
-  - pytorch
+- nvidia
+- pytorch
+base_model:
+- Qwen/Qwen2.5-14B-Instruct
 ---
 
 # OpenCodeReasoning-Nemotron-14B Overview
 
-## Description 
+## Description: <br>
+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. <br>
 
-OpenCodeReasoning-Nemotron-14B is a large language model (LLM) which is a derivative of [Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-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/non-commercial use. <br>
 
-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.](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-internal-scientific-research-and-development-model-license/)
+## Results from [OpenCodeReasoning](https://arxiv.org/abs/2504.01943)
 
-### Deployment Geography:
-Global<br>
+Below results are the average of **64 evaluations** on each benchmark.
 
-### Use Case: <br>
-This model is intended for developers and researchers building LLMs. <br>
+| 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)
 
-### Release Date:  <br>
-Huggingface [04/25/2025] via https://huggingface.co/nvidia/OpenCodeReasoning-Nemotron-14B/ <br>
 
+## How to use the models?
 
-## References
-- [\[2504.01943\] OpenCodeReasoning: Advancing Data Distillation for Competitive Coding](https://arxiv.org/abs/2504.01943)
+To run inference on coding problems:
 
+```python
+import transformers
+import torch
 
-## Model Architecture
-- Architecture Type: Dense decoder-only Transformer model
-- Network Architecture: Qwen2.5-14B-Instruct 
+model_id = "nvidia/OpenCodeReasoning-Nemotron-14B"
 
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model_id,
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    device_map="auto",
+)
 
-## Input
-- **Input Type(s):** Text <br>
-- **Input Format(s):** String <br>
-- **Input Parameters:** One-Dimensional (1D) <br>
-- **Other Properties Related to Input:** Context length up to 32,768 tokens <br>
+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'])
+
+```
 
-## Output
-- **Output Type(s):** Text <br>
-- **Output Format:** String <br>
-- **Output Parameters:** One-Dimensional (1D) <br>
-- **Other Properties Related to Output:** Context length up to 32,768 tokens <br> 
 
-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. <br> 
+## 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
 
-## Software Integration
-* Runtime Engine: Transformers, vLLM <br>
+## Model Architecture: <br>
+Architecture Type: Dense decoder-only Transformer model
+Network Architecture: Qwen-14B-Instruct 
+<br>
+**This model was developed based on Qwen2.5-14B-Instruct and has 14B model parameters. <br>**
+**OpenCodeReasoning-Nemotron-14B was developed based on Qwen2.5-14B-Instruct and has 14B model parameters. <br>**
+
+## Input: <br>
+**Input Type(s):** Text <br>
+**Input Format(s):** String <br>
+**Input Parameters:** One-Dimensional (1D) <br>
+**Other Properties Related to Input:** Context length up to 32,768 tokens <br>
+
+## Output: <br>
+**Output Type(s):** Text <br>
+**Output Format:** String <br>
+**Output Parameters:** One-Dimensional (1D) <br>
+**Other Properties Related to Output:** Context length up to 32,768 tokens <br> 
+
+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. <br>   
+
+## Software Integration : <br>
+* Runtime Engine: NeMo 2.3.0 <br>
 * Recommended Hardware Microarchitecture Compatibility: <br>
-  - NVIDIA Ampere
-  - NVIDIA Hopper
+NVIDIA Ampere <br>
+NVIDIA Hopper <br>
 * Preferred/Supported Operating System(s): Linux <br> 
 
-
-## Model Version(s) 
+## Model Version(s): 
 1.0 (4/25/2025)  <br>
+OpenCodeReasoning-Nemotron-7B<br>
+OpenCodeReasoning-Nemotron-14B<br>
+OpenCodeReasoning-Nemotron-32B<br>
+OpenCodeReasoning-Nemotron-32B-IOI<br>
 
 
-## Training Dataset
-The training corpus for OpenCodeReasoning-Nemotron-14B 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 <br>
-* Data Labeling Method: Hybrid: Automated, Human, Synthetic <br>
-
+# Training and Evaluation Datasets: <br>   
 
-## Evaluation Dataset
-We used the datasets listed in the next section to evaluate OpenCodeReasoning-Nemotron-14B. <br>
-* Data Collection Method: Hybrid: Automated, Human, Synthetic <br>
-* Data Labeling Method: Hybrid: Automated, Human, Synthetic <br>
+## Training Dataset:
 
+The training corpus for OpenCodeReasoning-Nemotron-14B is [OpenCodeReasoning](https://huggingface.co/datasets/nvidia/OpenCodeReasoning) dataset, which is composed of competitive programming questions and DeepSeek-R1 generated responses. 
 
-### [LiveCodeBench](https://huggingface.co/datasets/livecodebench/code_generation_lite)
-| Easy  | Medium | Hard | Avg. |
-|:------|:------|:------|:-----|
-| 97.6  | 74.4  | 27.6  | 59.4 |
+Data Collection Method: Hybrid: Automated, Human, Synthetic <br>
+Labeling Method: Hybrid: Automated, Human, Synthetic <br>
+Properties: 736k samples from OpenCodeReasoning (https://huggingface.co/datasets/nvidia/OpenCodeReasoning)
 
-### [CodeContests](https://huggingface.co/datasets/deepmind/code_contests)
-| Public  | Private | Generated | All |
-|:--------|:--------|:----------|:----|
-|   57.1  |   34.5  |    40.2   | 23.6|
+## Evaluation Dataset:
+We used the datasets listed in the next section to evaluate OpenCodeReasoning-Nemotron-14B. <br>
+**Data Collection Method: Hybrid: Automated, Human, Synthetic <br>**
+**Labeling Method: Hybrid: Automated, Human, Synthetic <br>**
 
 
-## Inference
-- **Engine:** vLLM <br>
-- **Test Hardware** NVIDIA H100-80GB <br>
 
+### License/Terms of Use: <br> 
+GOVERNING TERMS: Use of this model is governed by [Apache 2.0](https://huggingface.co/nvidia/OpenCode-Nemotron-2-14B/blob/main/LICENSE).
 
-## Ethical Considerations:
+### Deployment Geography:
+Global<br>
 
-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. 
+### Use Case: <br>
+This model is intended for developers and researchers building LLMs. <br>
 
-For more detailed information on ethical considerations for this model, please see the Model Card++ [Explainability](./EXPLAINABILITY.md), [Bias](./BIAS.md), [Safety & Security](./SAFETY_and_SECURITY.md), and [Privacy](./PRIVACY.md) Subcards.
+### Release Date:  <br>
+Huggingface [04/25/2025] via https://huggingface.co/nvidia/OpenCodeReasoning-Nemotron-7B/ <br> 
 
-Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
+## Reference(s):
+[2504.01943] OpenCodeReasoning: Advancing Data Distillation for Competitive Coding
+<br>
 
+## Inference:
+**Engine:** vLLM <br>
+**Test Hardware** NVIDIA H100-80GB <br>
 
-## Citation
+## 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.  
 
-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}, 
-}
+Please report security vulnerabilities or NVIDIA AI Concerns here.