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.

Tutorial·Difficulty: Intermediate·10 chapters·Updated Apr 18, 2026

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

FileDescription
01-what-is-huggingface.mdWhat Hugging Face is, why it matters, and the big picture
02-core-products.mdThe Hub, models, datasets, Spaces, inference, and account basics
03-using-the-hub.mdHow to search, evaluate, download, and compare resources on the website
04-python-quickstart.mdInstall the core libraries and run models in Python
05-essential-libraries.mdTransformers, Datasets, Tokenizers, Evaluate, and Accelerate
06-fine-tuning-and-training.mdFine-tuning, PEFT/LoRA, trainers, and training workflow
07-deployment-and-sharing.mdSpaces, Inference Endpoints, the Hub, and deployment options
08-common-workflows.mdPractical workflows for NLP, vision, audio, RAG, and agents
09-best-practices-and-risks.mdSecurity, licensing, evaluation, cost, and quality control
10-reference.mdQuick 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