Gain a comprehensive understanding of quantization essentials with Hugging Face in this module. Quantization, a crucial optimization technique in machine learning, involves reducing numerical precision to enhance efficiency without compromising accuracy. Through this module, participants will explore fundamental concepts, techniques, and implementation strategies essential for effective quantization.
Dive into various quantization techniques supported by Hugging Face, including post-training quantization, quantization-aware training, and dynamic quantization. Hands-on exercises and coding examples will guide participants in implementing quantization techniques, optimizing model inference performance, and deploying quantized models in real-world scenarios.
Evaluate the performance of quantized models and analyze their impact on accuracy, inference speed, and memory usage. Discover best practices for quantization implementation and avoid common pitfalls. Real-world applications and case studies will illustrate the benefits of quantization across diverse domains, from computer vision to natural language processing.
