This repository provides a comprehensive, free course for learning about Large Language Models (LLMs), targeting individuals from foundational math and Python skills to advanced LLM development and application engineering. It offers structured roadmaps, detailed explanations, and practical Colab notebooks to guide users through building, fine-tuning, quantizing, and deploying LLMs, as well as creating LLM-powered applications.

How It Works

The course is structured into three main paths: "LLM Fundamentals" (optional math, Python, neural networks), "LLM Scientist" (building LLMs, covering Transformers, pre-training, fine-tuning, quantization), and "LLM Engineer" (LLM applications, RAG, deployment, security). It leverages a wide array of open-source tools and libraries, providing hands-on examples through Colab notebooks and linking to extensive resources for deeper dives into each topic.

Quick Start & Requirements

• Installation: Primarily uses Google Colab notebooks, requiring no local installation for many examples. Specific tools mentioned (e.g., mergekit, axolotl, llama.cpp, Ollama) may require Python environments or Docker.
• Prerequisites: Basic Python, mathematics (linear algebra, calculus, statistics), and neural network fundamentals are recommended for the "Scientist" and "Engineer" paths. Access to GPUs is beneficial for fine-tuning and running larger models locally.
• Resources: Links to official documentation, tutorials, and interactive assistants are provided throughout the README.

Highlighted Details

• Comprehensive Curriculum: Covers LLM fundamentals, advanced training techniques (SFT, DPO), quantization methods (GGUF, GPTQ), RAG, agents, and deployment strategies.
• Practical Tools: Features notebooks for tools like AutoEval, LazyMergekit, LazyAxolotl, AutoQuant, and ZeroSpace for simplified LLM operations.
• End-to-End Focus: Guides users from understanding LLM architecture and pre-training to building production-ready applications and securing them.
• Extensive References: Each section includes curated links to articles, videos, and other repositories for further learning.

Maintenance & Community

The project is maintained by mlabonne, with acknowledgments to contributors for input and resources. Community interaction channels are not explicitly listed, but the author's X and Hugging Face profiles are provided.

Licensing & Compatibility

The repository content itself appears to be freely available for educational purposes. Specific tools and libraries mentioned within the course will have their own licenses.

Limitations & Caveats

While the course aims to be comprehensive, some advanced topics like distributed training and large-scale deployment may require significant computational resources beyond typical Colab environments. The rapid evolution of LLM technology means some specific tools or techniques might be updated frequently.