This post provides a detailed guide on quantization for large language models, explaining its benefits, and demonstrating how to apply it using PyTorch. It covers the definition and necessity of quantization, various methods like asymmetric and symmetric quantization, and includes step-by-step coding instructions for implementing quantization and de-quantization on model weight parameters.
Table of contents
A simple guide to teach you intuition about quantization with simple mathematical derivation and coding in PyTorch.1. What is quantization and why do you need it?2. How does quantization work? A simple mathematical derivation.3. Writing code in PyTorch to perform quantization and de-quantization of LLM weight parameters.Sort: