# Hugging Face LLM Course Source: https://freeaicourses.com/courses/hugging-face-llm-course Last checked: 2026-10-05 (cost, chapters, time estimate, prerequisites, certificate status, setup, authors and what Hugging Face suggests next) ## Short answer Our Hugging Face LLM Course review: the Hugging Face LLM Course is the best free course for coders who want to work with language models using the Transformers library. A Hugging Face team teaches 12 chapters of about 6 to 8 hours each, and it costs nothing. There is no certificate yet, and it is not a first course. ## Key facts - Authors: A Hugging Face team, including Abubakar Abid, Ben Burtenshaw, Matthew Carrigan, Lysandre Debut and Sylvain Gugger - Length: 12 chapters, each designed for 1 week at about 6 to 8 hours - Prerequisite: Good Python; better after an introductory deep learning course - Cost: Free, no ads - Certificate: None yet; the chapter 1 "Certification exam" is a quiz - Setup: Google Colab notebook (recommended for beginners) or a Python virtual environment - Account: Most of the course relies on a Hugging Face account - Libraries: Transformers, Datasets, Tokenizers, Accelerate and the Hugging Face Hub ## Who it is for - Coders who know Python and want to use language models through the Hugging Face libraries - Learners who have finished an introductory deep learning course such as fast.ai's Practical Deep Learning for Coders - Anyone who wants to fine-tune a model and share it on the Hugging Face Hub ## Who should skip it - Anyone without good Python: Hugging Face asks for it - Learners who want a certificate: the course offers none yet - Anyone short on time: 12 chapters at 6 to 8 hours each is 72 to 96 hours ## What you can do afterwards - Explain how Transformer models work and use a model from the Hugging Face Hub - Fine-tune a pretrained model on your own data - Use the Datasets and Tokenizers libraries, including building your own tokeniser - Build and share demos of your models on the Hub - Fine-tune large language models and build reasoning models ## What the course covers - Chapter 1: Transformer models: How Transformer models work, which the next chapters build on. - Chapter 2: Using Transformers: Use a model from the Hugging Face Hub. - Chapter 3: Fine-tuning a pretrained model: Fine-tune a model on your own data. - Chapter 4: Sharing models and tokenisers: Share your results on the Hugging Face Hub. - Chapter 5: The Datasets library: Load and work with data using the Datasets library. - Chapter 6: The Tokenizers library: How tokenisation works, using the Tokenizers library. - Chapter 7: Classical NLP tasks: Classic NLP tasks and LLM techniques. - Chapter 8: How to ask for help: How to ask for help when your code does not work. - Chapter 9: Building and sharing demos: Build demos of your models and share them on the Hub. - Chapter 10: Curating high-quality datasets: Curate high-quality datasets. - Chapter 11: Fine-tuning large language models: Fine-tune large language models. - Chapter 12: Building reasoning models: Build reasoning models. Hugging Face marks this chapter as new. ## What works - It is completely free and without ads, according to Hugging Face - It teaches the libraries themselves: Transformers, Datasets, Tokenizers and Accelerate - Colab notebooks give you free GPUs for smaller workloads, so you need no hardware - It covers the whole path from using a model to sharing demos and fine-tuning large models - Chapter 12, on building reasoning models, is marked new ## What does not - There is no certificate: Hugging Face says it is working on a certification programme - It is not a first course: it asks for good Python and works best after a deep learning course - 72 to 96 hours is a big commitment, at 6 to 8 hours for each of the 12 chapters - Most of the course relies on a Hugging Face account - Windows is not covered for a local setup, so Windows users rely on Colab ## The catch There is no certificate yet. The "Certification exam" in chapter 1 is a short multiple-choice quiz that needs a Hugging Face sign-in, not a certificate. The course is also not a first course: it asks for good Python and works best after an introductory deep learning course. At 6 to 8 hours for each of the 12 chapters, it is 72 to 96 hours of work. ## Is it still current? The course pages were checked on 5 October 2026. The course began as an NLP course and now puts its emphasis on large language models, and chapter 12, on building reasoning models, is marked new. ## Take before - Practical Deep Learning for Coders - Machine Learning Specialization ## Take after Neural Networks: Zero to Hero ## Verdict Take the Hugging Face LLM Course once you know Python and, ideally, have finished an introductory deep learning course, and you want to use and fine-tune language models in code. It scores 8.6: hands-on is 9 and truly free is 10, because the course is free with no ads, while clarity and depth are 8 and up to date is 9. It offers no certificate, and 72 to 96 hours is a big commitment. ## Questions people ask ### Is the Hugging Face LLM Course free? Yes. Hugging Face describes it as completely free and without ads. Most of the course relies on a Hugging Face account, and there is no certificate yet. ### How long does the Hugging Face LLM Course take? Each chapter is designed to take 1 week at about 6 to 8 hours of work. With 12 chapters, that is 72 to 96 hours, and this site lists 72 hours, the low end. You can take as long as you need. ### What do you need before the Hugging Face LLM Course? You need good Python. Hugging Face says the course is better after an introductory deep learning course, such as fast.ai's Practical Deep Learning for Coders or a DeepLearning.AI programme. You do not need prior PyTorch or TensorFlow, though familiarity helps. ### Does the Hugging Face LLM Course give a certificate? No. Hugging Face says it has no certification for the course yet and is working on a certification programme. The chapter 1 "Certification exam" is a short multiple-choice quiz that needs a Hugging Face sign-in. ### Do you need a GPU for the Hugging Face LLM Course? No. Hugging Face recommends a Google Colab notebook for beginners, and Colab gives GPUs free for smaller workloads. You can also use a Python virtual environment, but the course does not cover Windows for that. ### What should you take after the Hugging Face LLM Course? Hugging Face recommends DeepLearning.AI's Natural Language Processing Specialization. For a different angle, Neural Networks: Zero to Hero builds a GPT from scratch in code. ## Related pages - [Neural Networks: Zero to Hero](https://freeaicourses.com/courses/neural-networks-zero-to-hero) - [Practical Deep Learning for Coders](https://freeaicourses.com/courses/practical-deep-learning-for-coders) - [Machine Learning Specialization](https://freeaicourses.com/courses/machine-learning-specialization) - [Hugging Face Learn](https://freeaicourses.com/platforms/hugging-face) - [Build apps with LLMs](https://freeaicourses.com/paths/build-with-llms) - [All free AI courses, rated](https://freeaicourses.com/courses)