Welcome fastai to the Hugging Face Hub

Summary

Hugging Face Hub has integrated with the fastai deep learning library, allowing practitioners to upload and download models directly through the huggingface_hub Python client. Fastai Learner objects, which bundle model architectures, data loaders, and loss functions, can be published to a designated repository namespace using push_to_hub_fastai. Uploaded models automatically receive Git-based version control, Git LFS support for large files, and generated model cards. Practitioners can retrieve and instantiate published models locally for inference or transfer learning via from_pretrained_fastai. The integration also supports Blurr, an ecosystem library that combines fastai with Hugging Face Transformers for sequence classification and related text modeling tasks.

Context

Fastai practitioners needed a streamlined way to publicly share, discover, version-control, and load trained Learner models and transfer learning weights within the broader machine learning community.

Approach / What changed

Hugging Face introduced integration utilities in the huggingface_hub Python library, adding push_to_hub_fastai and from_pretrained_fastai functions to enable single-line uploading and downloading of fastai and Blurr models.

Takeaways

• Fastai Learner models can be published to the Hugging Face Hub using push_to_hub_fastai after authenticating via token, CLI, or notebook_login.
• Hosted fastai models on the Hub automatically include Git-based version control with Git LFS for large file storage and auto-generated model cards.
• The from_pretrained_fastai function downloads Hub models directly into local fastai Learner instances, supporting standard vision models as well as Blurr Transformer pipelines.

Related reading

• huggingface.co · Jul 13, 2021

  Welcome spaCy to the Hugging Face Hub

  Hugging Face has integrated spaCy pipelines into the Hugging Face Hub to simplify the distribution, testing, and deployment of natural language processing models across the community. More than 60 canonical models from the spaCy 3.1 release are hosted in the spaCy organization, alongside discoverable community pipelines. Users can package and upload models directly through the command line using the spacy-huggingface-hub extension, which auto-generates required metadata and model cards. Hosted pipelines offer direct pip installation from live wheel URLs, interactive browser exploration, and production-ready HTTP endpoints through the Inference API. The platform provides immediate interactive widget support for named entity recognition components, with upcoming support planned for text classification and part-of-speech tagging pipelines.

  Omar Sanseviero, Ines Montani

• huggingface.co · Jan 21, 2022

  Welcome Stable-baselines3 to the Hugging Face Hub 🤗

  Hugging Face announced an official integration with Stable-Baselines3, a popular PyTorch library for training and testing Deep Reinforcement Learning agents across diverse environments like Gym, Atari, MuJoco, and Procgen. The integration enables researchers and developers to host their saved reinforcement learning checkpoints on the Hugging Face Hub and download pre-trained community models. Interacting with the Hub requires installing the huggingface_hub and huggingface_sb3 packages, which supply helper methods for authentication, downloading, and uploading. Practitioners can retrieve checkpoint zip files using the load_from_hub function by providing the target repository identifier and filename before loading them into Stable-Baselines3 algorithms. Furthermore, users authenticated via CLI or notebook login can train policies such as PPO and publish their saved zip files to the Hub using push_to_hub.