Update README.md

README.md

@@ -13,190 +13,197 @@ tags:
 pipeline_tag: text-generation
 ---
 
-# OpenCodeReasoning-Nemotron-32B Overview
+# Model Card for Model ID
 
-## Description: <br>
-OpenCodeReasoning-Nemotron-32B is a large language model (LLM) which is a derivative of Qwen2.5-32B-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>
+<!-- Provide a quick summary of what the model is/does. -->
 
-This model is ready for commercial/non-commercial use. <br>
-
-![Evaluation Results](./results.png)
+This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
 
+## Model Details
 
-## Results from [OpenCodeReasoning](https://arxiv.org/abs/2504.01943)
+### Model Description
 
-Below results are the average of **64 evaluations** on each benchmark.
+<!-- Provide a longer summary of what this model is. -->
 
-| 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)
-
-
-## How to use the models?
-
-To run inference on coding problems:
 
-````python
-import transformers
-import torch
 
-model_id = "nvidia/OpenCodeReasoning-Nemotron-32B"
-
-pipeline = transformers.pipeline(
-    "text-generation",
-    model=model_id,
-    model_kwargs={"torch_dtype": torch.bfloat16},
-    device_map="auto",
-)
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
 
-prompt = """You are a helpful and harmless assistant. You should think step-by-step before responding to the instruction below.
+### Model Sources [optional]
 
-Please use python programming language only.
+<!-- Provide the basic links for the model. -->
 
-You must use ```python for just the final solution code block with the following format:
-```python
-# Your code here
-```
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
 
-{user}
-"""
+## Uses
 
-messages = [
-    {
-        "role": "user",
-        "content": prompt.format(user="Write a program to calculate the sum of the first $N$ fibonacci numbers")},
-]
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
-outputs = pipeline(
-    messages,
-    max_new_tokens=32768,
-)
-print(outputs[0]["generated_text"][-1]['content'])
+### Direct Use
 
-````
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
+[More Information Needed]
 
+### Downstream Use [optional]
 
-## Citation
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 
-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: <br>
-Architecture Type: Dense decoder-only Transformer model
-Network Architecture: Qwen-32B-Instruct 
-<br>
-**This model was developed based on Qwen2.5-32B-Instruct and has 32B model parameters. <br>**
-**OpenCodeReasoning-Nemotron-32B was developed based on Qwen2.5-32B-Instruct and has 32B 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>
+[More Information Needed]
 
-## 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> 
+### Out-of-Scope Use
 
-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>   
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
-## Software Integration : <br>
-* Runtime Engine: NeMo 2.3.0 <br>
-* Recommended Hardware Microarchitecture Compatibility: <br>
-NVIDIA Ampere <br>
-NVIDIA Hopper <br>
-* Preferred/Supported Operating System(s): Linux <br> 
+[More Information Needed]
 
-## 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>
+## Bias, Risks, and Limitations
 
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-# Training and Evaluation Datasets: <br>   
+[More Information Needed]
 
-## Training Dataset:
+### Recommendations
 
-The training corpus for OpenCodeReasoning-Nemotron-32B is [OpenCodeReasoning](https://huggingface.co/datasets/nvidia/OpenCodeReasoning) dataset, which is composed of competitive programming questions and DeepSeek-R1 generated responses. 
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
 
-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)
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 
-## Evaluation Dataset:
-We used the datasets listed in the next section to evaluate OpenCodeReasoning-Nemotron-32B. <br>
-**Data Collection Method: Hybrid: Automated, Human, Synthetic <br>**
-**Labeling Method: Hybrid: Automated, Human, Synthetic <br>**
+## How to Get Started with the Model
 
-### License/Terms of Use: <br> 
-GOVERNING TERMS: Use of this model is governed by [Apache 2.0](https://huggingface.co/nvidia/OpenCode-Nemotron-2-7B/blob/main/LICENSE).
+Use the code below to get started with the model.
 
-### Deployment Geography:
-Global<br>
+[More Information Needed]
 
-### Use Case: <br>
-This model is intended for developers and researchers building LLMs. <br>
+## Training Details
 
-### Release Date:  <br>
-Huggingface [04/25/2025] via https://huggingface.co/nvidia/OpenCodeReasoning-Nemotron-32B/ <br> 
+### Training Data
 
-## Reference(s):
-[2504.01943] OpenCodeReasoning: Advancing Data Distillation for Competitive Coding
-<br>
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
 
-## Inference:
-**Engine:** vLLM <br>
-**Test Hardware** NVIDIA H100-80GB <br>
+[More Information Needed]
 
-## 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.  
+### Training Procedure
 
-Please report security vulnerabilities or NVIDIA AI Concerns here.
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]