Duplicate from nvidia/NitroGen

Co-authored-by: nvlm <nvlm@users.noreply.huggingface.co>

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BIAS.md

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+Field                                                                                               |  Response
+:---------------------------------------------------------------------------------------------------|:---------------
+Participation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing:  |  Not Applicable
+Measures taken to mitigate against unwanted bias:                                                   |  Not Applicable

EXPLAINABILITY.md

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+Field                                                                                                  |  Response
+:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------
+Intended Task/Domain:                                                                   |  Vision-to-action model designed to play video games directly from raw frames
+Model Type:                                                                                            |  Transformer
+Intended Users:                                                                                        |  Researchers, game developers, open source community, gamers. Potential applications include next-generation game AI, automating testing for video games, and generally advancing research in embodied AI.
+Output:                                                                                                |  Gamepad actions
+Describe how the model works:                                                                          |  Image inputs are encoded with a vision transformer. A separate diffusion transformer is conditioned on the image embeddings, which then denoise an action tensor 
+Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of:  |  Not Applicable
+Technical Limitations & Mitigation:                                                                    |  This model performs well on games played with a gamepad. Model may not perform well on games played with a keyboard or mouse.
+Verified to have met prescribed NVIDIA quality standards:  |  Yes
+Performance Metrics:                                                                                   |  Task success rate 
+Potential Known Risks:                                                                                 |  The model may occasionally lose at certain games.
+Licensing:                                                                                             |  Governing Terms:  [NVIDIA License](https://developer.download.nvidia.com/licenses/NVIDIA-OneWay-Noncommercial-License-22Mar2022.pdf).  Additional Information:  [Apache License](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md) for [https://huggingface.co/google/siglip2-base-patch16-224]().

LICENSE

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+NVIDIA License
+
+1. Definitions
+
+“Licensor” means any person or entity that distributes its Work.
+“Work” means (a) the original work of authorship made available under this license, which may include software, documentation, or other files, and (b) any additions to or derivative works  thereof  that are made available under this license.
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+Works are “made available” under this license by including in or with the Work either (a) a copyright notice referencing the applicability of this license to the Work, or (b) a copy of this license.
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+
+3.3 Use Limitation. The Work and any derivative works thereof only may be used or intended for use non-commercially.  As used herein, “non-commercially” means for non-commercial research purposes only, and excludes any military, surveillance, service of nuclear technology or biometric processing purposes.
+
+3.4 Patent Claims. If you bring or threaten to bring a patent claim against any Licensor (including any claim, cross-claim or counterclaim in a lawsuit) to enforce any patents that you allege are infringed by any Work, then your rights under this license from such Licensor (including the grant in Section 2.1) will terminate immediately.
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+3.7  Components Under Other Licenses.  The Work may include or be distributed with components provided with separate legal notices or terms that accompany the components, such as open source software licenses and other license terms, including but not limited to the Meta OPT-IML 175B License Agreement (“Other Licenses”).  The components are subject to the applicable Other Licenses, including any proprietary notices, disclaimers, requirements and extended use rights; except that this Agreement will prevail regarding the use of third-party software, unless a third-party software license requires it license terms to prevail.
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+4. Disclaimer of Warranty.
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+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE OR NON-INFRINGEMENT. YOU BEAR THE RISK OF UNDERTAKING ANY ACTIVITIES UNDER THIS LICENSE. 
+
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+
+EXCEPT AS PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL THEORY, WHETHER IN TORT (INCLUDING NEGLIGENCE), CONTRACT, OR OTHERWISE SHALL ANY LICENSOR BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF OR RELATED TO THIS LICENSE, THE USE OR INABILITY TO USE THE WORK (INCLUDING BUT NOT LIMITED TO LOSS OF GOODWILL, BUSINESS INTERRUPTION, LOST PROFITS OR DATA, COMPUTER FAILURE OR MALFUNCTION, OR ANY OTHER DAMAGES OR LOSSES), EVEN IF THE LICENSOR HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.

PRIVACY.md

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+Field                                                                                                                              |  Response
+:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------
+Generatable or reverse engineerable personal data?                                                     |  No
+Personal data used to create this model?                                                                                       |  No
+Was consent obtained for any personal data used?                                                                                             |  Not Applicable
+How often is dataset reviewed?                                                                                                     |  During dataset creation and model training
+Was data from user interactions with the AI model (e.g. user input and prompts) used to train the model? |  No
+Is there provenance for all datasets used in training?                                                                                |  Yes
+Does data labeling (annotation, metadata) comply with privacy laws?                                                                |  Yes
+Is data compliant with data subject requests for data correction or removal, if such a request was made?                           |  No, not possible with externally-sourced data.
+Applicable Privacy Policy        | https://www.nvidia.com/en-us/about-nvidia/privacy-policy/ 

README.md

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+---
+license: other
+datasets:
+- nvidia/NitroGen
+tags:
+- behavior
+- cloning
+- gaming
+- agent
+pipeline_tag: reinforcement-learning
+---
+
+<img src="https://cdn-uploads.huggingface.co/production/uploads/67d8509cb6b70254852d734d/u3VY6_KoT6tEs86YPehU2.gif" width="100%" />
+
+<div align="center">
+  <p style="font-size: 1.2em;">
+    <a href="https://nitrogen.minedojo.org/"><strong>Website</strong></a> | 
+    <a href="https://github.com/MineDojo/NitroGen"><strong>Code</strong></a> |
+    <a href="https://huggingface.co/datasets/nvidia/NitroGen"><strong>Dataset</strong></a> |
+    <a href="https://huggingface.co/papers/2601.02427"><strong>Paper</strong></a>
+  </p>
+</div>
+
+# NitroGen: An Open Foundation Model for Generalist Gaming Agents
+
+NitroGen is a unified vision-to-action foundation model designed to play video games directly from raw frames. It is a generalist agent trained via large-scale behavior cloning on 40,000 hours of gameplay across over 1,000 games. It maps RGB video footage to gamepad actions.
+
+NitroGen works best on games designed for gamepad controls (e.g., action, platformer, and racing games) and is less effective on games that rely heavily on mouse and keyboard (e.g., RTS, MOBA).
+
+## Sample Usage
+
+### Installation
+
+To use NitroGen, clone and install the repository:
+
+```bash
+git clone https://github.com/MineDojo/NitroGen.git
+cd NitroGen
+pip install -e .
+```
+
+### Inference
+
+1. **Download the checkpoint** from Hugging Face:
+```bash
+hf download nvidia/NitroGen ng.pt
+```
+
+2. **Start the inference server**:
+```bash
+python scripts/serve.py <path_to_ng.pt>  
+```
+
+3. **Run the agent** on the game of your choice (currently supports Windows games):
+```bash
+python scripts/play.py --process '<game_executable_name>.exe'
+```
+
+## Model Details
+
+- **Architecture:** Vision Transformer (SigLip2) + Diffusion Matching Transformer (DiT).
+- **Parameters:** $4.93 \times 10^8$.
+- **Inputs:** 256x256 RGB images.
+- **Outputs:** Gamepad actions (21x16 shape: two 2D continuous vectors for joysticks, 17 binary buttons).
+- **Training:** Trained on 40,000 hours of internet-scale gameplay videos.
+
+## Citation
+
+If you find NitroGen useful in your research, please cite:
+
+```bibtex
+@misc{magne2026nitrogen,
+      title={NitroGen: An Open Foundation Model for Generalist Gaming Agents}, 
+      author={Loïc Magne and Anas Awadalla and Guanzhi Wang and Yinzhen Xu and Joshua Belofsky and Fengyuan Hu and Joohwan Kim and Ludwig Schmidt and Georgia Gkioxari and Jan Kautz and Yisong Yue and Yejin Choi and Yuke Zhu and Linxi "Jim" Fan},
+      year={2026},
+      eprint={2601.02427},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2601.02427}, 
+}
+```
+
+## License
+
+Governing Terms: [NVIDIA License](https://developer.download.nvidia.com/licenses/NVIDIA-OneWay-Noncommercial-License-22Mar2022.pdf).
+The model uses a [SigLip2](https://huggingface.co/google/siglip2-base-patch16-224) backbone which is licensed under Apache 2.0.

SAFETY.md

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+Field                                               |  Response
+:---------------------------------------------------|:----------------------------------
+Model Application Field(s):                               |  Media & Entertainment
+Describe the life critical impact (if present).   |  Not Applicable 
+Use Case Restrictions:                              |  Abide by [NVIDIA License](https://developer.download.nvidia.com/licenses/NVIDIA-OneWay-Noncommercial-License-22Mar2022.pdf).  Additional Information:  [Apache License](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md) for [https://huggingface.co/google/siglip2-base-patch16-224]().
+Model and dataset restrictions:            |  The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development.  Restrictions enforce dataset access during training, and dataset license constraints adhered to.

ng.pt

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