The Hugging Face Course Github

Leo Migdal

-Nov 18, 2025, 2:45 AM

This repo contains the content that's used to create the Hugging Face course. The course teaches you about applying Transformers to various tasks in natural language processing and beyond. Along the way, you'll learn how to use the Hugging Face ecosystem — 🤗 Transformers, 🤗 Datasets, 🤗 Tokenizers, and 🤗 Accelerate — as well as the Hugging Face Hub. It's completely free and open-source! As part of our mission to democratise machine learning, we'd love to have the course available in many more languages! Please follow the steps below if you'd like to help translate the course into your language 🙏.

To get started, navigate to the Issues page of this repo and check if anyone else has opened an issue for your language. If not, open a new issue by selecting the Translation template from the New issue button. Once an issue is created, post a comment to indicate which chapters you'd like to work on and we'll add your name to the list. Since it can be difficult to discuss translation details quickly over GitHub issues, we have created dedicated channels for each language on our Discord server. If you'd like to join, follow the instructions at this channel 👉: https://discord.gg/JfAtkvEtRb This is a practical course on using the Hugging Face ecosystem for machine learning.

You'll learn how to use the Hugging Face Hub, debug and troubleshoot machine learning models, and build interactive demos with Gradio. This course provides a practical, hands-on approach to working with the Hugging Face ecosystem for machine learning. The Hugging Face ecosystem has become the standard for machine learning practitioners working with transformer models and beyond. Learning these tools offers several advantages: Before starting, ensure you have the following: This course is designed to be followed along with hands-on coding.

You can choose from several approaches: If you like the course, don't hesitate to ⭐ star this repository. This helps us to make the course more visible 🤗. The course is divided into 4 units. These will take you from the basics of agents to a final assignment with a benchmark. Sign up here (it's free) 👉 https://bit.ly/hf-learn-agents

You can access the course here 👉 https://hf.co/learn/agents-course If you want to contribute to this course, you're welcome to do so. Feel free to open an issue or join the discussion in the Discord. For specific contributions, here are some guidelines: and get access to the augmented documentation experience This course will teach you about large language models (LLMs) and natural language processing (NLP) using libraries from the Hugging Face ecosystem — 🤗 Transformers, 🤗 Datasets, 🤗 Tokenizers, and 🤗 Accelerate — as...

We’ll also cover libraries outside the Hugging Face ecosystem. These are amazing contributions to the AI community and incredibly useful tools. While this course was originally focused on NLP (Natural Language Processing), it has evolved to emphasize Large Language Models (LLMs), which represent the latest advancement in the field. Throughout this course, you’ll learn about both traditional NLP concepts and cutting-edge LLM techniques, as understanding the foundations of NLP is crucial for working effectively with LLMs. View how beam search decoding works, in detail! View how beam search decoding works, in detail!

Answer questions using web search, weather, and guest info Test your knowledge of the Agent fundamentals. Generate Certificate of Excellence from Agents Course and get access to the augmented documentation experience Welcome to the comprehensive (and smollest) course to Fine-Tuning Language Models! This free course will take you on a journey, from beginner to expert, in understanding, implementing, and optimizing fine-tuning techniques for large language models.

This course is smol but fast! It’s for software developers and engineers looking to fast track their LLM fine-tuning skills. If that’s not you, check out the LLM Course. At the end of this course, you’ll understand how to fine-tune language models effectively and build specialized AI applications using the latest fine-tuning techniques. This is the repository for the LinkedIn Learning course Build with AI: Executing and Evaluating Hugging Face Models. The full course is available from LinkedIn Learning.

See the readme file in the main branch for updated instructions and information. This repository has branches for each of the videos in the course. You can use the branch pop up menu in github to switch to a specific branch and take a look at the course at that stage, or you can add /tree/BRANCH_NAME to the URL... The branches are structured to correspond to the videos in the course. The naming convention is CHAPTER#_MOVIE#. As an example, the branch named 02_03 corresponds to the second chapter and the third video in that chapter.

Some branches will have a beginning and an end state. These are marked with the letters b for "beginning" and e for "end". The b branch contains the code as it is at the beginning of the movie. The e branch contains the code as it is at the end of the movie. The main branch holds the final state of the code when in the course. When switching from one exercise files branch to the next after making changes to the files, you may get a message like this:

This document provides a technical overview of the Hugging Face Course repository, its structure, and how it operates. The course is designed to teach students about the Hugging Face ecosystem, with a focus on large language models (LLMs), natural language processing (NLP), and how to use libraries like 🤗 Transformers, 🤗 Datasets,... The Hugging Face Course is an open-source educational resource focused on teaching students how to apply Transformer models to various tasks in natural language processing and beyond. The course is completely free and available in multiple languages. The repository at https://github.com/huggingface/course contains all the content used to build the course website at https://huggingface.co/course. It includes text explanations, code snippets, diagrams, and utilities for generating Jupyter notebooks from the course content.

Sources: README.md1-4 chapters/en/chapter1/1.mdx10-14 The course repository is organized into several main directories:

The Hugging Face Course Github

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