Host Your WristWorld Model On Hugging Face!

Hey everyone,

I'm Niels from the open-source team at Hugging Face. I wanted to reach out about a really cool project I saw featured in our daily papers: WristWorld by @XuWuLingYu! You can check it out here: https://huggingface.co/papers/2510.07313.

For those who don't know, WristWorld is a novel 4D world model that generates wrist-view videos. The abstract mentions that the code and weights are available, which is awesome! I noticed that there isn't a direct download link for the pre-trained model checkpoints in the GitHub repository (https://github.com/XuWuLingYu/WristWorld) just yet.

Why Host on Hugging Face?

I was wondering if you'd be interested in hosting the pre-trained WristWorld model on Hugging Face Models once it's officially released. Hosting on Hugging Face can really boost the visibility of your model and make it easier for people to find and use. We can add relevant tags to the model cards, like "image-to-video" and "robotics," and also link it directly to the paper page. This will help researchers and developers find your amazing work more easily.

Hugging Face is a fantastic platform for sharing and discovering machine learning models. It offers a wide range of tools and resources to help you showcase your work and connect with the broader AI community. Hosting your WristWorld model on Hugging Face can significantly increase its impact and reach, allowing more people to benefit from your research. By leveraging the platform's features, such as model cards, tags, and links to your paper, you can ensure that your model is easily discoverable and accessible to a global audience. This can lead to increased citations, collaborations, and real-world applications of your work. Moreover, Hugging Face provides a supportive environment for open-source projects, offering resources and guidance to help you succeed. By joining the Hugging Face community, you can connect with other researchers, developers, and enthusiasts, fostering a collaborative ecosystem that drives innovation in the field of AI.

Easy Uploading and Integration

We've got a guide here to help you upload your model. If you're using a custom PyTorch model, the PyTorchModelHubMixin class is super handy. It adds from_pretrained and push_to_hub to your model, making it easy to upload and for others to download and use it right away. If you prefer a more direct approach, you can also use hf_hub_download to upload your model through the UI or any other method you like.

Uploading your model to Hugging Face is a straightforward process, thanks to the platform's user-friendly tools and comprehensive documentation. The PyTorchModelHubMixin class simplifies the integration of your custom PyTorch models, enabling seamless uploading and downloading. This feature is particularly beneficial for researchers and developers who want to share their models with the community without having to worry about the complexities of model serialization and deserialization. Additionally, the hf_hub_download tool provides a flexible alternative for those who prefer to upload their models through the UI or other methods. This ensures that you have the freedom to choose the approach that best suits your needs. Once your model is uploaded, you can easily manage its settings, add metadata, and track its usage, all within the Hugging Face platform.

Linking to Your Paper

After uploading, we can link the models to the paper page (read here) so people can easily find your model when they're reading about your work. This creates a seamless connection between your research and its practical implementation, making it easier for others to build upon your findings.

Linking your model to your paper is a crucial step in ensuring that your research has a lasting impact. By connecting your model to the corresponding paper page, you make it easier for researchers and developers to understand the context and motivation behind your work. This can lead to increased citations, collaborations, and real-world applications of your model. Hugging Face provides a simple and intuitive way to link your model to your paper, allowing you to showcase the connection between your theoretical contributions and practical implementations. This not only enhances the discoverability of your model but also helps to establish your credibility as a researcher. By making your model readily available and easily accessible, you can encourage others to build upon your work and contribute to the advancement of the field.

Build a Demo with Spaces and Free GPU Grants

You can even build a demo for your model on Spaces (https://huggingface.co/spaces)! We can also provide you with a ZeroGPU grant (https://huggingface.co/docs/hub/en/spaces-gpus#community-gpu-grants), which gives you access to A100 GPUs for free. This is a fantastic opportunity to showcase the capabilities of your WristWorld model and make it accessible to a wider audience.

Building a demo for your model is an excellent way to demonstrate its capabilities and make it more accessible to a wider audience. Hugging Face Spaces provides a platform for creating interactive demos that allow users to experiment with your model in a user-friendly environment. By showcasing your model in action, you can highlight its strengths and demonstrate its potential applications. This can be particularly valuable for attracting potential collaborators, investors, and users. Moreover, the ZeroGPU grant program offers free access to A100 GPUs, which can significantly accelerate the development and deployment of your demo. This allows you to create a high-quality demo without having to worry about the cost of infrastructure. By combining the power of Hugging Face Spaces with the resources of the ZeroGPU grant program, you can create a compelling showcase for your WristWorld model and maximize its impact.

So, what do you think? Are you interested? Let me know if you have any questions or need any guidance. I'm here to help!

Best,

Niels

External Resources:

For more information on sharing your models and datasets, visit the official Hugging Face Hub documentation: https://huggingface.co/docs/hub/en/repositories