LLM Course — open source GitHub project

LLM Course is a learning roadmap for large language models covering fundamentals, fine-tuning, quantization, evaluation, notebooks, and engineering scenarios.

What it is

LLM Course is Maxime Labonne’s learning roadmap for large language models. It combines roadmaps, articles, Colab notebooks, and sections for foundations, model research, and LLM engineering.

The project does not reduce LLMs to one tutorial. It covers math, Python, neural networks, NLP, architectures, pre-training, supervised fine-tuning, preference alignment, evaluation, quantization, multimodal models, and running LLMs.

What is inside

It contains LLM Fundamentals, The LLM Scientist, The LLM Engineer, and notebooks for evaluation, fine-tuning, quantization, and other practical tasks.

A practical flow is to choose the current role and gap: foundations for beginners, post-training and evaluation for researchers, deployment and APIs for engineers.

Learning route

This snippet shows the course logic from base knowledge to research and engineering.

Language: Markdown

1. LLM Fundamentals
2. The LLM Scientist
3. The LLM Engineer
4. Notebooks for practice

Strengths and limits

The strength is structure in a fast-moving field. Instead of a chaotic link list, it provides a topic map and practical notebooks.

The limitation is freshness. LLM tools, models, and techniques change quickly, so dates, model repositories, hardware needs, and licenses must be checked.