Hi @AiEson 🤗
Niels here from 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/2511.13647.
The paper page lets people discuss about your paper and lets them find artifacts about it (your models, datasets or demo for instance), you can also claim
the paper as yours which will show up on your public profile at HF, add Github and project page URLs.
Your paper introduces the novel Part-X-MLLM model and the UniPart-Bench dataset. It'd be great to make these checkpoints and dataset available on the 🤗 hub, to improve their discoverability/visibility.
We can add tags so that people find them when filtering https://huggingface.co/models and https://huggingface.co/datasets.
Uploading models
See here for a guide: https://huggingface.co/docs/hub/models-uploading.
In this case, we could leverage the PyTorchModelHubMixin class which adds from_pretrained and push_to_hub to any custom nn.Module. Alternatively, one can leverages the hf_hub_download one-liner to download a checkpoint from the hub.
We encourage researchers to push each model checkpoint to a separate model repository, so that things like download stats also work. We can then also link the checkpoints to the paper page.
For Part-X-MLLM, given it's a 3D multimodal large language model taking RGB point clouds and natural language prompts to generate part-level descriptions and edit commands, a suitable pipeline tag would be image-text-to-text.
Uploading dataset
Would be awesome to make the UniPart-Bench dataset available on 🤗 , so that people can do:
from datasets import load_dataset
dataset = load_dataset("your-hf-org-or-username/your-dataset")See here for a guide: https://huggingface.co/docs/datasets/loading.
Besides that, there's the dataset viewer which allows people to quickly explore the first few rows of the data in the browser.
For UniPart-Bench, as a part-centric benchmark with geometric and linguistic metrics for 3D multimodal tasks, relevant task categories could include image-to-3d and text-to-3d.
Let me know if you're interested/need any help regarding this!
Cheers,
Niels
ML Engineer @ HF 🤗
Hi @AiEson 🤗
Niels here from 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/2511.13647.
The paper page lets people discuss about your paper and lets them find artifacts about it (your models, datasets or demo for instance), you can also claim
the paper as yours which will show up on your public profile at HF, add Github and project page URLs.
Your paper introduces the novel Part-X-MLLM model and the UniPart-Bench dataset. It'd be great to make these checkpoints and dataset available on the 🤗 hub, to improve their discoverability/visibility.
We can add tags so that people find them when filtering https://huggingface.co/models and https://huggingface.co/datasets.
Uploading models
See here for a guide: https://huggingface.co/docs/hub/models-uploading.
In this case, we could leverage the PyTorchModelHubMixin class which adds
from_pretrainedandpush_to_hubto any customnn.Module. Alternatively, one can leverages the hf_hub_download one-liner to download a checkpoint from the hub.We encourage researchers to push each model checkpoint to a separate model repository, so that things like download stats also work. We can then also link the checkpoints to the paper page.
For Part-X-MLLM, given it's a 3D multimodal large language model taking RGB point clouds and natural language prompts to generate part-level descriptions and edit commands, a suitable pipeline tag would be
image-text-to-text.Uploading dataset
Would be awesome to make the UniPart-Bench dataset available on 🤗 , so that people can do:
See here for a guide: https://huggingface.co/docs/datasets/loading.
Besides that, there's the dataset viewer which allows people to quickly explore the first few rows of the data in the browser.
For UniPart-Bench, as a part-centric benchmark with geometric and linguistic metrics for 3D multimodal tasks, relevant task categories could include
image-to-3dandtext-to-3d.Let me know if you're interested/need any help regarding this!
Cheers,
Niels
ML Engineer @ HF 🤗