Introduction

Welcome to Hugging Face 101! This course will teach you the essentials of the Hugging Face ecosystem — home to hundreds of thousands of models, datasets, and interactive demos.

You’ll learn three ways to work with Hugging Face: through the web interface (no coding required), via APIs (for quick integration), or with Python libraries (for full control). Pick the approach that fits your project, or mix and match as needed!

What You’ll Learn

By the end of this course, you’ll know how to:

• Navigate the Hub: Find and use the models, datasets, and demos you need
• Use AI APIs: Integrate text generation, image creation, and more into your projects
• Share Your Work: Upload models, datasets, and demos for others to use
• Build Interactive Demos: Create Spaces that showcase your AI projects
• Evaluate Models: Test AI models to see if they work for your use case
• Use Advanced Features: Explore newer capabilities like the Model Context Protocol (MCP)

Course Structure

This course is divided into 8 chapters, each with a focus on a different aspect of Hugging Face.

1. Introduction
2. Introduction to Hugging Face - Learn about the Hugging Face ecosystem and how to get started
3. Inference Providers - Learn about the different ways to use Hugging Face models
4. Sharing Models - Learn how to share your own models with the community
5. Sharing Datasets - Learn how to share your own datasets with the community
6. Building Spaces - Learn how to build interactive demos with Hugging Face Spaces
7. Building with Gradio - Learn how to build interactive demos with Gradio
8. Evaluating Models - Learn how to evaluate AI models

Why Learn Hugging Face?

Hugging Face has become the go-to platform for AI development. Think of it as the GitHub for AI - where the community shares models, datasets, and applications. Recent innovations like the Model Context Protocol (MCP) are making AI models even more powerful by connecting them to external tools and data.

Whether you’re new to AI, a developer adding AI features to your app, or a researcher sharing your work, Hugging Face gives you the tools and community to succeed.

Choose Your Learning Path

This course works for everyone, from complete beginners to experienced developers. Pick the path that matches your style:

🖥️ UI-First : Start with Web Interfaces

Perfect if you’re new to AI or prefer clicking over coding

• Explore models through the Hub’s web interface
• Try AI models directly in your browser
• Create Spaces without writing code
• Focus on: Chapters 1, 3, 4, parts of 6

🔌 API-First : Integration Through APIs

Ideal if you want to add AI to existing apps quickly

• Use REST APIs to integrate AI models
• Test models with simple HTTP requests
• Deploy models with Inference Endpoints
• Focus on: Chapters 1, 2, 5, 7

🐍 Code-First : Deep Dive with Python

Best if you want full control and customization

• Install Hugging Face libraries locally
• Fine-tune models for your specific needs
• Build advanced applications with Python
• Focus on: All chapters, hands-on coding

🌟 Mix and Match

The flexible approach - use whatever works for each project

Start with the web interface, add API calls when needed, then dive into code for advanced features.

What You Need to Get Started

Everyone needs:

• A Hugging Face account (free)
• An internet browser
• Curiosity about AI!

For API and Code paths, also helpful:

• Basic familiarity with web APIs (we’ll explain as we go)
• Some Python experience (but beginners can follow along)

Using Google Colab (Recommended for Beginners)

For Code-First and API-First paths. UI-First learners can skip to Chapter 1.

Google Colab is the easiest way to get started - no installation required! Just open a notebook in your browser and start coding.

New to Colab? Check out their introduction first. Colab gives you free access to GPUs and TPUs for AI experiments.

Ready? Create a new notebook and let’s install the libraries:

Install the Hugging Face libraries with pip. In Colab, use ! before pip commands:

# Core libraries for this course
!pip install huggingface_hub transformers datasets gradio

# Optional: MCP client and tooling support
!pip install -U "huggingface_hub[mcp]"

Test that everything installed correctly:

import huggingface_hub
import transformers
import datasets
import gradio

print("✅ Ready to go!")

What Each Library Does

• huggingface_hub: Your connection to the Hub — handles authentication, downloads, uploads. MCP support is available via the optional mcp extra
• transformers: Thousands of pre-trained models for text, images, audio, and more
• datasets: Easy access to datasets and tools for processing your own data
• gradio: Build interactive web demos with just a few lines of code

To use the Model Context Protocol (MCP) client and tiny-agents, install the mcp extra: pip install -U "huggingface_hub[mcp]".

You’re now ready for all the projects in this course!

Local Python Setup (Advanced)

For experienced developers who want to work locally instead of Colab.

If you prefer local development, first install Python from python.org or follow this detailed guide.

Verify Python is installed:

python --version

We recommend using virtual environments to avoid package conflicts. Think of them as isolated Python setups for different projects.

Create a project directory and virtual environment:

# Create and enter your project directory
mkdir ~/huggingface-course
cd ~/huggingface-course

# Create virtual environment
python -m venv .env

# Activate it (Mac/Linux)
source .env/bin/activate

# On Windows, use:
# .env\Scripts\activate

When activated, your terminal prompt will change to show (.env). To deactivate later, just run deactivate.

Install the Libraries

Now install the same Hugging Face libraries:

pip install huggingface_hub transformers datasets gradio
pip install -U "huggingface_hub[mcp]"  # Optional MCP support

Test the installation:

python -c "import huggingface_hub, transformers, datasets, gradio; print('✅ Ready to go!')"

Get Your Access Token

To upload models, create private repos, and access gated models, you’ll need to authenticate:

from huggingface_hub import login
login()

This will ask for your Hugging Face token. Get yours from huggingface.co/settings/tokens.

What’s Next?

You’re ready to start! Here’s where to go based on your chosen path:

• UI-First: Jump to Chapter 1 and start exploring the Hub in your browser
• API-First: Chapter 1 for context, then Chapter 2 for your first API calls
• Code-First: Follow all chapters in order for the complete experience
• Mix and Match: Start with Chapter 1, then pick what you need

The great thing about Hugging Face is that all these approaches work together. Start simple, then add complexity as your projects grow.

Let’s get started.