Tutorial
Hugging Face Tutorial
A practical tutorial series on the Hugging Face ecosystem: what it is, how to use it, and how to go from browsing models to shipping real AI workflows. Covers the Hub, core libraries, fine-tuning, and deployment.
Chapters
About this tutorial
A practical tutorial series on the Hugging Face ecosystem: what it is, how to use it, and how to go from browsing models to shipping real AI workflows.
Contents
| File | Description |
|---|---|
| 01-what-is-huggingface.md | What Hugging Face is, why it matters, and the big picture |
| 02-core-products.md | The Hub, models, datasets, Spaces, inference, and account basics |
| 03-using-the-hub.md | How to search, evaluate, download, and compare resources on the website |
| 04-python-quickstart.md | Install the core libraries and run models in Python |
| 05-essential-libraries.md | Transformers, Datasets, Tokenizers, Evaluate, and Accelerate |
| 06-fine-tuning-and-training.md | Fine-tuning, PEFT/LoRA, trainers, and training workflow |
| 07-deployment-and-sharing.md | Spaces, Inference Endpoints, the Hub, and deployment options |
| 08-common-workflows.md | Practical workflows for NLP, vision, audio, RAG, and agents |
| 09-best-practices-and-risks.md | Security, licensing, evaluation, cost, and quality control |
| 10-reference.md | Quick reference, glossary, and a sensible learning roadmap |
Prerequisites
- Basic command-line usage
- Basic Python if you want to use the libraries directly
- Comfortable reading model cards and documentation
Quick Start
# 1) Install the main Python libraries
pip install -U transformers datasets tokenizers evaluate accelerate peft huggingface_hub
# 2) Log in if you want to download gated/private models or push your own work
hf auth login
# 3) Run a pipeline in Python
python3 - <<'PY'
from transformers import pipeline
classifier = pipeline("sentiment-analysis")
print(classifier("Hugging Face makes model usage much easier."))
PY
What You Will Learn
By the end of this series, you should be able to:
- Explain what Hugging Face does and where it fits in the AI stack
- Navigate the Hub and judge whether a model or dataset is worth using
- Use pretrained models in Python with minimal setup
- Fine-tune models for your own tasks when prompting is not enough
- Share demos, models, and datasets with other people
- Avoid common mistakes around licensing, evaluation, privacy, and deployment