Quantization Fundamentals with Hugging Face and Quanto

Running large language models efficiently requires optimizing their size without sacrificing performance. This text-only course introduces you to the core principles of model quantization, enabling you to run models on hardware with limited resources. You will start by understanding the foundational mathematics behind compression before moving into practical implementation. Learn to load, analyze, and quantize models using Hugging Face Transformers and the Quanto library. What you'll learn: Understand the core concepts of post-training quantization, calibration, and precision loss; Master the implementation of 8-bit and 4-bit quantization techniques; Apply the Quanto library to compress open-source models; Configure Hugging Face Transformers to load quantized weights efficiently; Practice troubleshooting common quantization bottlenecks and accuracy degradation. This course begins with essential terminology and mathematical foundations of precision mapping before guiding you through step-by-step written code walkthroughs. It is designed for software engineers, data scientists, and machine learning beginners who want to optimize models without needing expensive hardware. Start learning today and make your machine learning models faster and lighter.