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README.md

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+---
+# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
+# Doc / guide: https://huggingface.co/docs/hub/model-cards
+{}
+---
 
+# Model Card for Model ID
 
+<!-- Provide a quick summary of what the model is/does. -->
 
----
-library_name: transformers
-license: other
-license_name: nvidia-open-model-license
-license_link: >-
-  https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
-pipeline_tag: text-generation
-language:
-  - en
-tags:
-  - nvidia
-  - reasoning
-  - math
-  - code
-  - reinforcement learning
-  - pytorch
----
+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
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **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]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### 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]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- 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. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- 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:**
 
-# AceReason-Nemotron: Advancing Math and Code Reasoning through Reinforcement Learning
+[More Information Needed]
 
- <img src="fig/main_fig.png" alt="main_fig" style="width: 600px; max-width: 100%;" />
+## Glossary [optional]
 
-We're thrilled to introduce AceReason-Nemotron-14B, a math and code reasoning model trained entirely through reinforcement learning (RL), starting from the DeepSeek-R1-Distilled-Qwen-14B. It delivers impressive results, achieving 78.6% on AIME 2024 (+8.9%), 67.4% on AIME 2025 (+17.4%), 61.1% on LiveCodeBench v5 (+8%), 54.9% on LiveCodeBench v6 (+7%), and 2024 on Codeforces (+543). We systematically study the RL training process through extensive ablations and propose a simple yet effective approach: first RL training on math-only prompts, then RL training on code-only prompts. Notably, we find that math-only RL not only significantly enhances the performance of strong distilled models on math benchmarks, but also code reasoning tasks. In addition, extended code-only RL further improves code benchmark performance while causing minimal degradation in math results. We find that RL not only elicits the foundational reasoning capabilities acquired during pre-training and supervised fine-tuning (e.g., distillation), but also pushes the limits of the model's reasoning ability, enabling it to solve problems that were previously unsolvable.
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
 
-We share our training recipe, training logs in our [technical report](https://arxiv.org/abs/2505.16400).
+[More Information Needed]
 
-## Results
+## More Information [optional]
 
-We evaluate our model against competitive reasoning models of comparable size within Qwen2.5 and Llama3.1 model family on AIME 2024, AIME 2025, LiveCodeBench v5 (2024/08/01 - 2025/02/01), and LiveCodeBench v6 (2025/02/01-2025/05/01). More evaluation results can be found in our [technical report](https://arxiv.org/abs/2505.16400).
+[More Information Needed]
 
-| **Model** | **AIME 2024<br>(avg@64)** | **AIME 2025<br>(avg@64)** | **LCB v5<br>(avg@8)** | **LCB v6<br>(avg@8)** |
-| :---: | :---: | :---: | :---: | :---: |
-| <small>QwQ-32B</small> | 79.5 | 65.8 | 63.4 | - |
-| <small>DeepSeek-R1-671B</small> | 79.8 | 70.0 | 65.9 | - |
-| <small>Llama-Nemotron-Ultra-253B</small> | 80.8 | 72.5 | 66.3 | - |
-| <small>o3-mini (medium)</small> | 79.6 | 76.7 | 67.4 | - |
-| <small>Light-R1-14B</small> | 74 | 60.2 | 57.9 | 51.5 |
-| <small>DeepCoder-14B (32K Inference)</small> | 71 | 56.1 | 57.9 | 50.4 |
-| <small>OpenMath-Nemotron-14B</small> | 76.3 | 63.0 | - | - |
-| <small>OpenCodeReasoning-Nemotron-14B</small> | - | - | 59.4 | 54.1 |
-| <small>Llama-Nemotron-Super-49B-v1</small> | 67.5 | 60.0 | 45.5 | - |
-| <small>DeepSeek-R1-Distilled-Qwen-14B</small> | 69.7 | 50.2 | 53.1 | 47.9 |
-| <small>DeepSeek-R1-Distilled-Qwen-32B</small> | 72.6 | 54.9 | 57.2 | - |
-| [AceReason-Nemotron-14B 🤗](https://huggingface.co/nvidia/AceReason-Nemotron-14B)| 78.6 | 67.4 | 61.1 | 54.9 |
+## Model Card Authors [optional]
 
+[More Information Needed]
 
+## Model Card Contact
 
+[More Information Needed]