Fine-Tuning and Optimizing Large Language Models

As large language models become central to modern software, general-purpose models often need customization to excel at specialized tasks. This text-only course provides a clear pathway to understanding how to adapt, fine-tune, and optimize these powerful models for your specific domain needs. You will transition from understanding core model architectures to confidently applying advanced optimization techniques. Through structured written explanations and step-by-step code walkthroughs, you will learn how to make models more efficient, accurate, and context-aware. What you'll learn: Understand foundational LLM concepts, architectures, and the mechanics of text generation; Apply parameter-efficient fine-tuning (PEFT) techniques including LoRA and QLoRA; Configure context engineering and retrieval-augmented generation (RAG) patterns; Optimize model performance and reduce resource requirements for deployment; Practice preparing datasets and formatting training data for optimal tuning results. The course begins with essential terminology and the mechanics of pre-trained models before guiding you through dataset preparation, fine-tuning methodologies, and modern optimization strategies. You will read through detailed code implementations and theoretical explanations designed to solidify your practical understanding. This course is designed for software developers, data enthusiasts, and technical beginners eager to customize AI models, with no prior machine learning experience required. Start reading today to unlock the full potential of custom language models.