Release MeshCoder artifacts (models, dataset) on Hugging Face

まず、どの MeshCoder チェックポイントと対応するオブジェクトコードデータセットがリリース可能な状態かを確認します。Hugging Face のモデルアップロードおよびデータセット読み込みガイドを、PyTorchModelHubMixin と load_dataset を含めて確認します。各チェックポイントに個別のモデルリポジトリがあり、データセットを Hub から読み込んで表示でき、成果物を論文にリンクできれば完了です。

Hi @ZhaoyangLyu 🤗

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/2508.14879.
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.

I saw on your GitHub repository that you plan to release the code for MeshCoder and the associated large-scale paired object-code dataset by November 2025. That's fantastic news!
It'd be great to make these checkpoints and the dataset available on the 🤗 hub once they are released, 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. The MeshCoder model (a multimodal LLM that translates 3D point clouds into Blender Python scripts) would likely fit an "image-to-3d" pipeline tag, and the "large-scale paired object-code dataset" would have an "image-to-3d" task category.

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.

Uploading dataset

Would be awesome to make the 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.

Let me know if you're interested/need any help regarding this once the artifacts are ready for release!

Cheers,

Niels
ML Engineer @ HF 🤗