Release MeshCoder artifacts (models, dataset) on Hugging Face

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 🤗

首先检查哪些 MeshCoder 检查点以及配套的对象代码数据集已经准备好发布。查看 Hugging Face 关于上传模型和加载数据集的指南，包括 PyTorchModelHubMixin 和 load_dataset。完成的标准是：每个检查点都有单独的模型仓库，数据集已在 Hub 上提供以供加载和查看，并且这些工件可以链接到论文。