# Hugging Face — reference material

*Placeholder.* This folder will cover Hugging Face: the de facto hub for open-weight AI models, datasets, and demos.

Planned topics:

- What Hugging Face is and why it matters (the "GitHub of AI models")
- Browsing the model hub: filtering by task, size, license
- Reading a model card: what to look for before you commit to a model
- Downloading and running a model with `transformers`
- The `pipeline()` shortcut for common tasks (text generation, transcription, classification, etc.)
- `sentence-transformers` for embeddings
- Where the weights actually live on your disk, and how to clean them up
- Datasets and Spaces (brief tour)
- Authentication and the few model families that need it (e.g. Llama)

## When to dip in

When your project needs a model — for transcription, summarization, image generation, embeddings, classification — and you'd rather run something locally than pay for an API.

## Prerequisites

Some [`../python/`](../python/), and ideally [`../pytorch/`](../pytorch/) if you want to peek under the hood.