mlabonne/llm-course

Project overview

A free, structured educational course covering Large Language Model theory and practical application from fundamentals to deployment, organized into three progressive sections with interactive Colab notebooks. It is intended for researchers, developers, and AI engineers seeking to learn or build LLM applications.

Project type

Model Development · Evaluation & Observability

Use cases

Learning & Education

Deployment

Refer to project documentation

License

Apache-2.0

Best for

• Researchers, developers, and AI engineers who want a structured approach to learning LLM theory and application.
• Learners who prefer interactive Colab notebooks over mandatory local environment setup.

Key capabilities

• Covers essential knowledge about mathematics, Python, and neural networks for LLMs.
• Focuses on creating LLM-based applications and deploying them.

Limitations and risks

• GPU, minimum hardware, and operating system requirements are not documented for this project.

Getting started

• Users can open a Colab notebook and execute the cells to begin consuming the course content.

Evidence and sources

• GitHub project description: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
• README: The LLM course is divided into three parts: 1. 🧩 **LLM Fundamentals** is optional and covers fundamental knowledge about mathematics, Python, and neural networks. 2. 🧑‍🔬 **The LLM…
• README: ⚡ AutoQuant | Quantize LLMs in GGUF, GPTQ, EXL2, AWQ, and HQQ formats in one click.
• README: Deploying LLMs at scale is an engineering feat that can require multiple clusters of GPUs. In other scenarios, demos and local apps can be achieved with much lower complexity.
• README: Fine-tune Llama 3.1 with Unsloth | Ultra-efficient supervised fine-tuning in Google Colab.

Reviewed at: 2026-07-17T04:50:11Z