AI Model Compression: Quantization, LoRA, and PEFT for Beginners

As artificial intelligence models grow larger, deploying them efficiently on standard hardware becomes a critical challenge. Understanding how to shrink these models without losing accuracy is an essential skill for modern AI developers and engineers. This text-based course guides you through the foundational concepts and practical techniques of AI model compression. You will transition from understanding basic model architectures to reading and implementing optimization strategies that make large models lighter, faster, and ready for deployment. What you'll learn: Understand the foundational concepts of model size, computational complexity, and the need for compression; Learn how quantization reduces precision to save memory and speed up inference; Apply parameter-efficient fine-tuning (PEFT) and Low-Rank Adaptation (LoRA) to adapt large models with minimal resources; Explore knowledge distillation to transfer capabilities from massive teacher models to compact student models; Practice analyzing compression trade-offs between model size, inference speed, and accuracy; Discover modern quantization formats and techniques suited for edge device deployment. The course begins with core definitions and the mathematics behind model size, then guides you step-by-step through quantization, distillation, and PEFT. Through clear written explanations and conceptual code walkthroughs, you will learn how to apply these techniques to real-world AI workflows. This course is designed for aspiring AI developers, software engineers, and tech enthusiasts who want to learn model optimization from scratch. No prior experience with model compression is required, though a basic understanding of Python and machine learning concepts is helpful. Start reading today to unlock the potential of lightweight, high-performance AI systems.