Release APM models and datasets on Hugging Face

Hi @bytedance 🤗

Niels here from the open-source team at Hugging Face. I discovered your work on Arxiv and was very impressed by your paper "An All-Atom Generative Model for Designing Protein Complexes" (https://huggingface.co/papers/2504.13075) and the release of your code and artifacts on Zenodo.

It'd be great to make the APM model checkpoints (Backbone, Sidechain, and Refine Modules) and the associated datasets (APM_full_data_90, cluster_0, metadata, swissprot_data, pdb_test_full, multimer_unclustered, and PepBench) available on the 🤗 Hub, to improve their discoverability and visibility within the broader AI community. We can add specific tags so that researchers interested in protein design and complex generation can easily find them on 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](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) class which adds `from_pretrained` and `push_to_hub` to any custom `nn.Module`. Alternatively, one can leverages the [hf_hub_download](https://huggingface.co/docs/huggingface_hub/en/guides/download#download-a-single-file) 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:

```python
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](https://huggingface.co/docs/hub/en/datasets-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!

Cheers,

Niels
ML Engineer @ HF 🤗

Start by reviewing the existing code and artifacts on Zenodo, then follow the Hugging Face model-uploading and dataset-loading guides. Done means the Backbone, Sidechain, and Refine Module checkpoints and the seven listed datasets are available on the Hub with appropriate tags and discoverable repositories.

Written by the indexing model from the issue text.