Course Description

Embark on a journey to master the art of compressing AI models using the Hugging Face Transformers library and the Quanto library. This course introduces learners to linear quantization, a streamlined and effective technique for reducing the size and enhancing the performance of models, including large language models and vision models. By exploring both linear quantization and downcasting techniques, participants will learn how to make advanced generative AI models more efficient and accessible across various devices.

What Students Will Learn

• Understand the basics and implementation details of linear quantization and its application across different types of AI models.
• Experience hands-on training in quantizing open-source multimodal and language models using the Quanto library.
• Learn how to employ the Transformers library to implement downcasting, thereby loading models at approximately half their standard size.
• Acquire practical skills in making generative AI models more resource-efficient, thereby facilitating their use on non-specialist hardware like smartphones and personal computers.

Prerequisites or Skills Necessary

To benefit fully from this course, learners should have:

• A basic understanding of machine learning concepts.
• Some practical experience with PyTorch.

Course Coverage

• Introduction to Model Quantization and its Necessity
• Deep Dive into Linear Quantization using the Quanto Library
• Practical Exercises in Quantizing Open Source Models
• Application of Downcasting in Model Compression
• Real-World Adaptations of Quantized Models

Who This Course is For

This course is tailored for individuals who are new to the concept of model quantization but possess a foundational knowledge of machine learning. It is ideal for those who aim to deepen their understanding of quantization within the realm of generative AI and seek practical experience in this transformative technology.

Real-World Applications of Skills

Upon completing this course, learners will be equipped to:

• Enhance the performance and reduce the operational costs of AI systems by implementing quantization techniques.
• Adapt large-scale AI models to run effectively on lower-specification devices, broadening the potential user base.
• Optimize AI applications for real-time processing on edge devices in various industry sectors, including healthcare, finance, and retail.