Benefits of Hosting Checkpoints on Hugging Face

Oct 13, 2025 by miodeya.com 125 views

Introduction

As a member of the open-source team at Hugging Face, I came across your work on Arxiv and was impressed by the innovative approach you've taken in your research. I'd like to extend an invitation to submit your paper to our paper page, which will not only improve its discoverability but also provide a platform for discussion and artifact sharing. Additionally, you can claim the paper as yours, showcasing your work on your public profile at Hugging Face, along with links to your GitHub and project pages.

Benefits of Hosting Checkpoints on Hugging Face

Hosting your ResMoE checkpoints on the Hugging Face hub will significantly enhance their visibility and discoverability. Our platform allows researchers to easily share and access pre-trained models, making it an ideal place to showcase your work. By leveraging the PyTorchModelHubMixin class or the hf_hub_download one-liner, you can seamlessly integrate your checkpoints with our hub, enabling users to download and utilize them with ease.

Why Host Checkpoints on Hugging Face?

Hosting your checkpoints on Hugging Face offers several benefits, including:

Improved discoverability: By hosting your checkpoints on our platform, you'll increase their visibility and make it easier for researchers to find and access them.
Easy sharing and collaboration: Our hub allows you to share your checkpoints with others, facilitating collaboration and knowledge sharing within the research community.
Download statistics: By hosting each model checkpoint in a separate repository, you'll be able to track download statistics, providing valuable insights into the usage and impact of your work.
Linking to paper page: We encourage researchers to link their checkpoints to the paper page, enabling users to easily access and discuss the related research.

Getting Started with Hugging Face

If you're interested in hosting your ResMoE checkpoints on Hugging Face, we've provided a step-by-step guide to help you get started:

Submit your paper: Share your paper on our paper page, which will improve its discoverability and provide a platform for discussion and artifact sharing.
Claim your paper: Claim your paper as yours, showcasing your work on your public profile at Hugging Face, along with links to your GitHub and project pages.
Upload your checkpoints: Use the PyTorchModelHubMixin class or the hf_hub_download one-liner to upload your checkpoints to our hub.
Link to paper page: Link your checkpoints to the paper page, enabling users to easily access and discuss the related research.

Additional Resources

For more information on how to upload your checkpoints to our hub, please refer to the following resources:

Hugging Face Hub Documentation: Our documentation provides a comprehensive guide on how to upload and manage models on our hub.
PyTorchModelHubMixin Class: This class adds from_pretrained and push_to_hub to any custom nn.Module, making it easy to integrate your checkpoints with our hub.
hf_hub_download One-Liner: This one-liner allows you to download a checkpoint from our hub, making it easy to access and utilize your models.

Conclusion

Hosting your ResMoE checkpoints on Hugging Face will not only improve their discoverability but also provide a platform for discussion and artifact sharing. By leveraging our hub and the PyTorchModelHubMixin class or the hf_hub_download one-liner, you can seamlessly integrate your checkpoints with our platform, enabling users to download and utilize them with ease. We encourage you to submit your paper and claim your work on our platform, showcasing your research to a wider audience.

Get in Touch

If you have any questions or need help regarding this process, please don't hesitate to reach out. We're here to support you and help you get the most out of our platform.

Introduction

As a member of the open-source team at Hugging Face, I'm excited to provide a Q&A article to help answer your questions about releasing ResMoE checkpoints on our platform. Whether you're a researcher, developer, or simply interested in learning more about our hub, this article will provide you with the information you need to get started.

Q: What is the Hugging Face Hub?

A: The Hugging Face Hub is a platform that allows researchers and developers to share, discover, and use pre-trained models. Our hub provides a simple and intuitive way to upload, manage, and access models, making it easier for users to find and utilize the models they need.

Q: Why should I host my ResMoE checkpoints on the Hugging Face Hub?

A: Hosting your ResMoE checkpoints on our hub will improve their discoverability, making it easier for researchers and developers to find and use your models. Additionally, our hub provides a platform for discussion and artifact sharing, enabling users to easily access and discuss the related research.

Q: How do I upload my ResMoE checkpoints to the Hugging Face Hub?

A: To upload your ResMoE checkpoints to our hub, you can use the PyTorchModelHubMixin class or the hf_hub_download one-liner. Our documentation provides a comprehensive guide on how to upload and manage models on our hub.

Q: What is the PyTorchModelHubMixin class?

A: The PyTorchModelHubMixin class is a mixin that adds from_pretrained and push_to_hub to any custom nn.Module. This makes it easy to integrate your checkpoints with our hub, enabling users to download and utilize your models with ease.

Q: What is the hf_hub_download one-liner?

A: The hf_hub_download one-liner is a simple way to download a checkpoint from our hub. This one-liner allows users to easily access and utilize your models, making it a convenient option for developers and researchers.

Q: How do I link my ResMoE checkpoints to the paper page?

A: To link your ResMoE checkpoints to the paper page, simply upload your checkpoints to our hub and then link them to the paper page. Our documentation provides a step-by-step guide on how to do this.

Q: Can I track download statistics for my ResMoE checkpoints?

A: Yes, you can track download statistics for your ResMoE checkpoints by hosting each model checkpoint in a separate repository. This will provide valuable insights into the usage and impact of your work.

Q: How do I claim my paper as mine on the Hugging Face Hub?

A: To claim your paper as yours on our hub, simply submit your paper to our paper page and then claim it as yours. This will showcase your work on your public profile at Hugging Face, along with links to your GitHub and project pages.

Q: What if I need help with uploading my ResMoE checkpoints to the Hugging Face Hub?

A: Don't worry, we're here to help! If you need assistance with uploading your ResMoE checkpoints to our hub, please don't hesitate to reach out. Our team is happy to provide support and guidance to help you get the most out of our platform.

Conclusion

Releasing your ResMoE checkpoints on the Hugging Face Hub is a great way to improve their discoverability and provide a platform for discussion and artifact sharing. By leveraging our hub and the PyTorchModelHubMixin class or the hf_hub_download one-liner, you can seamlessly integrate your checkpoints with our platform, enabling users to download and utilize them with ease. We hope this Q&A article has provided you with the information you need to get started.