Quantization Fundamentals with Hugging Face

Generative AI models, like large language models, often exceed the capabilities of consumer-grade hardware and are expensive to run. Compressing models through methods such as quantization makes them more efficient, faster, and accessible. This allows them to run on a wide variety of devices, including smartphones, personal computers, and edge devices, and minimizes performance degradation.

Join this course to:

• Quantize any open source model with linear quantization using the Quanto library.
• Get an overview of how linear quantization is implemented. This form of quantization can be applied to compress any model, including LLMs, vision models, etc.
• Apply “downcasting,” another form of quantization, with the Transformers library, which enables you to load models in about half their normal size in the BFloat16 data type.

By the end of this course, you will have a foundation in quantization techniques and be able to apply them to compress and optimize your own generative AI models, making them more accessible and efficient.

This is an introduction to the fundamental concepts of quantization for learners with a basic understanding of machine learning concepts and some experience with PyTorch, who is interested in learning about model quantization in generative AI.