Hi @adept-thu 馃
I'm Niels and work as part of the open-source team at Hugging Face. I discovered your work through Hugging Face's daily papers as yours got featured: https://huggingface.co/papers/2507.04049.
The paper page lets people discuss your paper and find artifacts related to it (like your models). You can also claim the paper as yours, which will show it on your public HF profile and allow you to link your GitHub and project pages directly.
I saw you've released the official code for DIVER on GitHub. Would you be interested in hosting your pre-trained model checkpoints on https://huggingface.co/models? Hosting on Hugging Face provides better visibility and discoverability for the autonomous driving community. We can add specific tags (like robotics) so people can find your work easily.
If you're interested, I'm leaving a guide here. If your implementation is in PyTorch, you could even use the PyTorchModelHubMixin class which adds from_pretrained and push_to_hub to the model, allowing people to download and use your models right away.
After uploading, we can also link the models to the paper page so people can discover your work more easily. We can also provide you a ZeroGPU grant, which gives you A100 GPUs for free if you'd like to build a demo on Spaces.
Let me know if you're interested or need any guidance :)
Kind regards,
Niels
ML Engineer @ HF 馃
Hi @adept-thu 馃
I'm Niels and work as part of the open-source team at Hugging Face. I discovered your work through Hugging Face's daily papers as yours got featured: https://huggingface.co/papers/2507.04049.
The paper page lets people discuss your paper and find artifacts related to it (like your models). You can also claim the paper as yours, which will show it on your public HF profile and allow you to link your GitHub and project pages directly.
I saw you've released the official code for DIVER on GitHub. Would you be interested in hosting your pre-trained model checkpoints on https://huggingface.co/models? Hosting on Hugging Face provides better visibility and discoverability for the autonomous driving community. We can add specific tags (like
robotics) so people can find your work easily.If you're interested, I'm leaving a guide here. If your implementation is in PyTorch, you could even use the PyTorchModelHubMixin class which adds
from_pretrainedandpush_to_hubto the model, allowing people to download and use your models right away.After uploading, we can also link the models to the paper page so people can discover your work more easily. We can also provide you a ZeroGPU grant, which gives you A100 GPUs for free if you'd like to build a demo on Spaces.
Let me know if you're interested or need any guidance :)
Kind regards,
Niels
ML Engineer @ HF 馃