ModelsFrameworksFine-Tuning

Hugging Face LLM Course

by Hugging Face

BeginnerCourseFree~15-20 hours, self-paced

The definitive free course on transformers and LLMs using the Hugging Face ecosystem.

Start LearningReviewed July 3, 2026

Overview

This course teaches modern NLP/LLM development through the Hugging Face libraries (Transformers, Datasets, Tokenizers). It covers how transformer models work, using pretrained models, fine-tuning on your data, tokenization internals, and increasingly agent/LLM-app topics. It's hands-on throughout, framework-current, and free with a certificate — a superb structured path for practitioners.

At a Glance

Topic
Models
Level
Beginner
Format
Course
Cost
Free
Duration
~15-20 hours, self-paced
Provider
Hugging Face
Hands-on
Yes — code/exercises
Certificate
Available

What You’ll Learn

  • How transformer models and tokenizers work
  • Using and fine-tuning pretrained models
  • The Transformers/Datasets/Tokenizers libraries
  • Building applications on top of LLMs

Highlights

  • Comprehensive and hands-on
  • Maintained by Hugging Face, kept current
  • Free with certificate

Who It’s For

Best For

  • Developers entering practical LLM/NLP work

Prerequisites

  • Python
  • Basic deep-learning concepts

FAQ

What is Hugging Face LLM Course?

Hugging Face's comprehensive free course (formerly the NLP Course) on transformers, tokenizers, fine-tuning, and building with LLMs.

Is Hugging Face LLM Course free?

Hugging Face LLM Course is free to access.

What level is Hugging Face LLM Course for?

Hugging Face LLM Course is aimed at a beginner audience. Recommended background: Python, Basic deep-learning concepts.

How long does Hugging Face LLM Course take?

Expect roughly ~15-20 hours, self-paced. Most learners work through it at their own pace.

What will I learn from Hugging Face LLM Course?

You'll learn: How transformer models and tokenizers work; Using and fine-tuning pretrained models; The Transformers/Datasets/Tokenizers libraries; Building applications on top of LLMs.

Topics

transformersNLPHugging Facefine-tuningtokenizers