LLM Course by Maxime Labonne is the curriculum most people who actually want to understand large language models end up at, not because it’s the flashiest, but because it’s structured as three real layers (fundamentals, scientist, engineer) with runnable Colab notebooks at every step. 80,000 stars from people who’d rather read code than marketing.
- 80,982 stars
- 9,439 forks
- Apache-2.0 License
LLM Course: Three layers, one through-line
Most LLM resources collapse “understanding models” and “shipping applications” into one tangled path. LLM Course separates them on purpose. Fundamentals is optional primer material: linear algebra, Python for ML, neural network basics. You skim it if you already know it. LLM Scientist is the deep end: model merging, quantization (GGUF, GPTQ, EXL2, AWQ), fine-tuning with QLoRA/ORPO/DPO, model distillation. LLM Engineer covers the production side: RAG architectures, serving, deployment, evaluation.
You can enter at whichever layer matches where you actually are. The structure assumes you don’t need to start from arithmetic if you already write Python for a living.
Notebooks you’ll actually reuse
The standout feature is the tools section, a collection of one-click Colab notebooks that solve specific painful tasks. LazyMergekit merges models without a GPU. AutoQuant quantizes a model in five formats in a single click. LazyAxolotl fine-tunes in the cloud without local setup. Model Family Tree visualizes how merged models descend from each other, surprisingly useful when you’re picking a base.
These aren’t toy demos. They’re the same scripts Maxime uses for his own Hugging Face work, packaged so other people can run them on a free Colab instance.
Born from a blog, not a bootcamp
The course grew out of Maxime’s blog, where each major topic has a long-form article alongside the Colab notebook. The README links the two together, so when a concept in a notebook needs more context, the matching article explains the why and the math. This is rare. Most course repos treat text as a comment block, not a real explanation.
The blog cadence also means the course gets updated as new techniques land. Recent additions cover abliteration (uncensoring models without retraining), Llama 3.1 fine-tuning with Unsloth, and MoE creation with MergeKit. Older material doesn’t get pruned, but the frontier keeps moving forward.
There’s a book if you want more
The course is and will remain free, but it’s the source material for the LLM Engineer’s Handbook (Packt, co-authored with Paul Iusztin). The book is the comprehensive version: end-to-end LLM application from design to deployment, with the production-grade architecture the course can only hint at. Worth knowing about, not required. The course stands alone.
A companion DeepWiki renders the repo as a navigable knowledge base, which is genuinely useful for search when you know what you’re looking for but not which notebook it lives in.
Get started
Star it, pick a layer, open the first notebook in Colab, no clone required to try it:
git clone https://github.com/mlabonne/llm-course.gitMost notebooks link directly to Open in Colab badges, so the clone is only needed if you want everything offline.
LLM Course earns its place on the shortlist of must-read LLM material. If you’ve been collecting links instead of building intuition, start with the Scientist track. Maxime’s notebooks are the fastest path from “I’ve heard of quantization” to actually shipping a 4-bit model.