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.

Takeaways

1. 1

   Fastai Learner models can be published to the Hugging Face Hub using push_to_hub_fastai after authenticating via token, CLI, or notebook_login.

2. 2

   Hosted fastai models on the Hub automatically include Git-based version control with Git LFS for large file storage and auto-generated model cards.

3. 3

   The from_pretrained_fastai function downloads Hub models directly into local fastai Learner instances, supporting standard vision models as well as Blurr Transformer pipelines.