LLM Optimization Basics: Compression and Fine-Tuning

Large Language Models (LLMs) are incredibly powerful, but their massive size often makes them difficult and expensive to run. How can you deploy these models on everyday hardware without sacrificing performance? This text-based course breaks down the complex world of LLM optimization into accessible, written lessons. You will explore how to shrink model sizes, speed up inference, and evaluate performance using modern industry techniques. By focusing on practical concepts, you will learn how to make heavy AI models lightweight and accessible. What you'll learn: Understand foundational LLM architecture and why model size impacts computational resources. Apply quantization techniques to reduce memory usage while maintaining text generation quality. Explore model pruning and knowledge distillation to conceptualize smaller, faster models. Practice parameter-efficient fine-tuning methods like LoRA and QLoRA for custom applications. Evaluate local LLM performance using modern benchmarking tools and metrics. Discover how optimized models integrate into modern Retrieval-Augmented Generation (RAG) pipelines. The course begins with essential terminology and the basic mechanics of neural network compression. From there, you will progress through structured reading materials and written exercises that cover fine-tuning methods and local deployment strategies. Designed for beginners and aspiring machine learning practitioners, this course requires no prior experience with advanced AI engineering. Start reading today to build your foundational skills in efficient AI deployment.