Implementation

A Coding Implementation on Introduction to Weight Quantization: Key Aspect in Enhancing Efficiency in Deep Learning and LLMs
In today’s deep learning landscape, optimizing models for deployment in resource-constrained environments is more important than ever. Weight quantization addresses this need by reducing the precision of model parameters, typically from 32-bit floating point values to lower bit-width representations, thus yielding smaller models that can run faster on hardware with limited resources. This tutorial introduces…