mlabonne/llm-course: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. ·
Based on this course, I co-wrote the LLM Engineer's Handbook, a hands-on book that covers an end-to-end LLM application from design to deployment. The LLM course will always stay free, but you can support my work by purchasing this book. For a more comprehensive version of this course, check out the DeepWiki. A list of notebooks and articles I wrote about LLMs. This section introduces essential knowledge about mathematics, Python, and neural networks. You might not want to start here but refer to it as needed. This section of the course focuses on learning how to build the best possible LLMs using the latest techniques. An in-depth knowledge of the Transformer architecture is not required, but it's important to understand the main steps of modern LLMs: converting text into numbers through tokenization, processing these tokens through layers including attention mechanisms, and finally generating new text through various sampling strategies. 📚 References: Pre-training is a computational