What it is
The LLM Course by Maxime Labonne is a free GitHub roadmap for learning large language models. It is split into three parts: fundamentals (maths, Python, neural networks, NLP), the LLM Scientist track (architecture, pre-training, post-training data, fine-tuning, preference alignment, evaluation, quantisation) and the LLM Engineer track (running models, vector storage, RAG, agents, inference optimisation, deployment, security).
Each section pairs a short explainer with curated articles, videos and papers. A separate table lists about two dozen Colab notebooks by the author, from one-click tools such as LazyMergekit and AutoQuant to fine-tuning walkthroughs. The same material underpins the LLM Engineer's Handbook, which Labonne co-wrote for Packt.
There are no releases. Commits have slowed to a handful a year; the latest, on 5 February 2026, fixed a broken link.
Our review
The LLM Course is a map, not a textbook, and it is a very good map. Its value is the order: it tells a developer what to learn after transformers, what post-training involves, and where RAG and agents fit, then links to the best free material for each step. About 84,600 GitHub stars suggest many people use it exactly that way.
The notebooks are the practical half and the part to check before relying on it. The newest fine-tuning walkthrough targets Llama 3.1 and was written in July 2024; others cover Mistral-7B with DPO and QLoRA, Llama 2 and CodeLlama. The techniques (LoRA, ORPO, GGUF quantisation, model merging, abliteration) still apply, but library versions and base models have moved on, so expect some cells to need fixing.
The written roadmap is more current than the notebooks: the engineer track cites EAGLE-3 speculative decoding and Hugging Face's Agents Course, but the commit history shows only five commits in the past twelve months, mostly small edits. It remains inside the 12-month activity window, just.
The course is free and will stay so, the author says, with the paid LLM Engineer's Handbook as the way to support it. Compared with Sebastian Raschka's LLMs from Scratch, which teaches by building one model end to end in code, this covers far more ground with far less depth per topic.
Where it earns its place
A backend engineer moving into an applied-AI role
Work out what to study, in what order, over a few months
Follow the LLM Engineer track section by section: running models, vector stores, RAG, agents, inference, deployment and security. Everything linked is free; the cost is time, and some links may have moved.
An ML engineer with one free-tier Colab GPU
Fine-tune a 7B model for the first time
Open the Mistral-7B QLoRA notebook, which the author wrote for free-tier Colab, then swap in a current base model. Expect to update library versions before the cells run cleanly.
A training lead onboarding three new hires to an LLM team
Give them a shared syllabus without buying a course
Use the Scientist track headings as the reading plan and the notebooks as exercises. It saves writing an internal curriculum, though the team will need to add current models and tooling itself.
Before you commit
- Setup
- Nothing to install: read the README on GitHub and open notebooks in Google Colab with a Google account. The Mistral-7B QLoRA notebook is written for Colab's free tier.
- Integrations
- Notebooks use Hugging Face Transformers, TRL, Unsloth, Axolotl, MergeKit, llama.cpp and ExLlamaV2; some push results to the Hugging Face Hub or rent GPUs on RunPod.
- Data and privacy
- Static Markdown and notebooks under Apache 2.0, so reuse in internal training is allowed. Running notebooks in Colab puts your data on Google's service, and Hub uploads need your own token.
- Cost at scale
- Free. The only spend is GPU time if you outgrow free Colab, for example a rented GPU for the Axolotl or AutoEval notebooks, plus the optional LLM Engineer's Handbook.
- Limits
- No releases, five commits in the past twelve months and 91 open issues or pull requests. Notebook model choices (Llama 2, Llama 3.1, Mistral-7B) are from 2023 and 2024.
What's good
- Clear order across fundamentals, Scientist and Engineer tracks
- About two dozen free Colab notebooks by the author
- Covers post-training, quantisation and merging in unusual depth
- Apache 2.0, free with no sign-up
What's not
- Mostly links to outside material
- Newest fine-tuning notebook dates from July 2024 (Llama 3.1)
- Five commits in the past year
Best for
Developers who want a structured path into LLM training and engineering.
Alternatives
LLM Course: common questions
- Is LLM Course worth it?
- A well-ordered syllabus for the whole LLM stack whose hands-on notebooks have not kept pace with its reading list. We rate it 4.2 out of 5.
- How much does LLM Course cost?
- Free and open source under the Apache 2.0 license.
- What are the downsides of LLM Course?
- Mostly links to outside material; newest fine-tuning notebook dates from July 2024 (Llama 3.1); five commits in the past year.
- Who is LLM Course best for?
- Developers who want a structured path into LLM training and engineering.
- How much does LLM Course cost for a team or in production?
- Free. The only spend is GPU time if you outgrow free Colab, for example a rented GPU for the Axolotl or AutoEval notebooks, plus the optional LLM Engineer's Handbook.
- Is LLM Course safe for company data?
- Static Markdown and notebooks under Apache 2.0, so reuse in internal training is allowed. Running notebooks in Colab puts your data on Google's service, and Hub uploads need your own token.
- What are the best LLM Course alternatives?
- The closest alternatives we have reviewed are LLMs from Scratch, Generative AI for Beginners and Prompt Engineering Guide.
Answers checked Oct 23, 2026.
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