Hi @cfifty 🤗
I'm Niels from the Hugging Face open-source team. I came across your ICLR 2025 paper, "Restructuring Vector Quantization with the Rotation Trick," via our daily papers feature: https://huggingface.co/papers/2410.06424. Your work on improving VQ-VAEs is very interesting!
I noticed you've released pretrained model checkpoints on Google Drive. To maximize their discoverability and impact, we'd love to see them hosted on the Hugging Face Hub. This would provide:
- Increased visibility: Researchers can easily find and use your models.
- Improved discoverability: We can add relevant tags to the model cards, making it easier for others to find them based on their use case.
- Integration with the Hugging Face ecosystem: Seamless integration with other tools and datasets on the Hub.
We have detailed guides for uploading models to the Hub: Model Uploading Guide. For PyTorch models, the PyTorchModelHubMixin simplifies the process: PyTorchModelHubMixin.
Would you be open to migrating your checkpoints to Hugging Face? Let me know if you have any questions or need assistance.
Best regards,
Niels
ML Engineer @ Hugging Face 🤗
Hi @cfifty 🤗
I'm Niels from the Hugging Face open-source team. I came across your ICLR 2025 paper, "Restructuring Vector Quantization with the Rotation Trick," via our daily papers feature: https://huggingface.co/papers/2410.06424. Your work on improving VQ-VAEs is very interesting!
I noticed you've released pretrained model checkpoints on Google Drive. To maximize their discoverability and impact, we'd love to see them hosted on the Hugging Face Hub. This would provide:
We have detailed guides for uploading models to the Hub: Model Uploading Guide. For PyTorch models, the
PyTorchModelHubMixinsimplifies the process: PyTorchModelHubMixin.Would you be open to migrating your checkpoints to Hugging Face? Let me know if you have any questions or need assistance.
Best regards,
Niels
ML Engineer @ Hugging Face 🤗