Overview
Dive deep into the world of Large Language Models (LLMs) with this comprehensive course. Designed for aspiring LLM scientists and engineers, this repository provides a structured learning path, practical roadmaps, and hands-on Google Colab notebooks to guide you from foundational concepts to building and deploying advanced LLM applications.
Key Features
- Structured Learning Paths: Choose between three distinct paths: "LLM Fundamentals" for essential prerequisites, "The LLM Scientist" for building and understanding LLMs, and "The LLM Engineer" for application development and deployment.
- Hands-on Colab Notebooks: Learn by doing with a rich collection of interactive Google Colab notebooks covering fine-tuning, quantization, model merging, and various LLM tooling.
- Practical Tools & Utilities: Discover easy-to-use tools like LazyMergekit, LazyAxolotl, and AutoQuant to streamline your LLM workflow.
- End-to-End LLM Engineering: Gain insights into creating, optimizing, and deploying LLM-based applications, including a detailed guide to building an LLM Engineer's Handbook.
- Foundational Knowledge: Optional modules cover the crucial mathematics, Python programming, neural network fundamentals, and NLP concepts needed to excel in LLMs.
Typical Use Cases
- Learning LLM Fundamentals: Beginners can build a strong foundation in math, Python, and neural networks essential for AI.
- Becoming an LLM Scientist: Researchers and developers can learn to build, fine-tune, and optimize state-of-the-art LLMs.
- Mastering LLM Engineering: Professionals can learn to integrate LLMs into applications, deploy them efficiently, and manage their lifecycle.
- Experimenting with LLM Tools: Quickly test and implement advanced LLM techniques using provided Colab notebooks for tasks like quantization, model merging, and automated evaluation.
- Building Custom LLM Solutions: Develop bespoke LLM applications tailored to specific needs, from chatbots to content generation tools.
