What is model quantization?

What is model quantization?

Model quantization is a technique used to reduce the size of machine learning models by converting them from high precision to lower precision formats.

Why is model quantization important?

It's important because it helps in reducing the memory footprint and computational requirements, making models faster and more efficient for deployment.

What are the common methods of model quantization?

Common methods include post-training quantization, quantization-aware training, and weight sharing.

Can you give an example of where model quantization is used?

Sure! It's often used in mobile applications and edge devices where computational resources are limited.