A notebook buffet for the HuggingFace ecosystem

What it does

This repository is a curated collection of Jupyter notebooks demonstrating how to use specific HuggingFace Transformers model classes. Each notebook targets a single architecture—BERT, DETR, Donut, CLIPSeg, and many others—and shows either inference or fine-tuning in PyTorch. It functions as a practical companion to the library’s documentation, organized by model name rather than by concept.

The interesting bit

The breadth is the point: it covers not just NLP staples but computer vision (object detection, depth estimation), audio (spectrogram transformers), and document AI (LayoutLM, DiT). Every notebook ships with an “Open in Colab” badge, so you can skip local setup entirely. It is essentially a searchable-by-model-name recipe box for the HuggingFace zoo.

Key highlights

• Broad collection of model-specific notebooks, from GPT-J to ConvNeXT
• Mix of tasks: NER, object detection, image segmentation, DocVQA, depth estimation, and audio classification
• All notebooks are Colab-ready and run without local installation
• Includes both quick inference and end-to-end fine-tuning examples
• Heavy emphasis on vision and document transformers, not just text

Caveats

• The README is a single massive bulleted list; finding a specific task means scrolling or using the browser’s find function
• Not a structured course—beginners are explicitly directed to HuggingFace’s separate free course
• PyTorch only; no TensorFlow or JAX equivalents

Verdict

Grab it when you need a working starter for a specific HuggingFace model class. Skip it if you are looking for a guided curriculum or a unified framework.