Hugging Face LLM Course review
Hugging Face · hugging-face
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.
What does it take?
| Difficulty | 3 out of 5 |
|---|---|
| Hours | About 72 hours |
| Format | Code notebooks, Reading |
| Maths needed | School-level maths |
| Coding needed | Yes, you will write code |
| Hardware needed | Free cloud GPU (Google Colab or Kaggle Notebooks) |
| Certificate | No certificate |
| Free to audit | Yes |
| Pace | Self-paced |
| Last updated by the provider | Not checked yet |
| 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 should take it, and who should skip it?
Take it if you are
- 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
Skip it if you are
- 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 will you be able to do after it?
- 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's in each module?
| Module | What it 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 we liked, and what we didn't
What we liked
- 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 we didn't
- 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
What's 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.
What should you take before it?
- Practical Deep Learning for Coders
Practical Deep Learning for Coders has 9 lessons of about 90 minutes, costs nothing and has no certificate. You deploy your own model by the end of lesson 2.
- Machine Learning Specialization
The Machine Learning Specialization has 3 courses and 95 hours of Andrew Ng's lessons. Coursera's FAQ says you cannot take it for free, so check the cost first.
What should you take after it?
- Neural Networks: Zero to Hero
Neural Networks: Zero to Hero is 8 free YouTube videos, about 14.5 hours, where Andrej Karpathy builds a GPT from scratch in code. No certificate.
How we rated it: 8.6 out of 10
| Criterion | Score out of 10 |
|---|---|
| Clarity | 8 |
| Hands-on practice | 9 |
| Up to date | 9 |
| Depth | 8 |
| Truly free | 10 |
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.
So should you take it?
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.
Where to go next
- Build apps with LLMs
A learning path that includes Hugging Face LLM Course.
- Hugging Face Learn: our platform review
What else it offers, and what costs money.
- Generative AI for Everyone
What large language models can actually do for you and your team, without writing a line of code. Andrew Ng teaches it in 6 hours on Coursera, certificate paid.
- Machine Learning Crash Course
Google's Machine Learning Crash Course has 12 modules and takes about 13.5 hours by Google's own timings. It is free, with quiz badges and no certificate.
- CS50's Introduction to AI with Python
CS50's Introduction to AI with Python runs 7 weeks with 12 projects, costs nothing and gives a free CS50 Certificate if you score 70% on every project.
Last checked : cost, chapters, time estimate, prerequisites, certificate status, setup, authors and what Hugging Face suggests next.
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