Quantization

Quantization is a technique used in machine learning and deep learning to reduce the precision of the numerical representations of model parameters and activations. By converting floating-point values to lower bit-width integers, such as INT8, quantization significantly decreases model size and memory bandwidth requirements, often leading to faster inference times. This process allows for high-performance computations on various hardware platforms while maintaining acceptable levels of accuracy. Quantization is particularly beneficial for deploying models on resource-constrained devices, enabling efficient use of computational resources without sacrificing performance.

Quantization

PyTorch documentation

This file is in the process of migration to torch/ao/quantization , and is kept here for compatibility while the migration process is ongoing. If you are adding a new entry/functionality, please, add ...

Dynamic Quantization

PyTorch Tutorials

Introduction There are a number of trade-offs that can be made when designing neural networks. During model developmenet and training you can alter the number of layers and number of parameters in a r...

A Visual Guide to Quantization

Towards Data Science

Demystifying the compression of large language models As their name suggests, Large Language Models (LLMs) are often too large to run on consumer hardware. These models may exceed billions of paramet...

quantize

PyTorch documentation

Quantize the input float model with post training static quantization. First it will prepare the model for calibration, then it calls run_fn which will run the calibration step, after that we will con...

Quantization in Machine Learning and Large Language Models

Towards AI

In this blog, we’ll dive deep into the different types of quantization, their significance, and practical examples to illustrate how they work. Numerical demonstrations are also included for better cl...

LLM Quantization Techniques- GPTQ

Towards AI

Recent advances in neural network technology have dramatically increased the scale of the model, resulting in greater sophistication and intelligence. Large Language Models (LLMs) have received high p...

Introduction to Weight Quantization

Towards Data Science

Reducing the size of Large Language Models with 8-bit quantization Large Language Models (LLMs) are known for their extensive computational requirements. Typically, the size of a model is calculated ...

Quantization: Making AI Models Lighter Without Sacrificing Performance

Python in Plain English

Behind Pay-Wall? Click here Medium Edit description medium.com The Weight of Intelligence Imagine trying to fit an elephant into a compact car. That’s similar to AI developers' challenges when deployi...

Quantization API Reference

PyTorch documentation

torch.quantization This module contains Eager mode quantization APIs. Top level APIs Quantize the input float model with post training static quantization. Converts a float model to dynamic (i.e. Do q...

Quantization Screencast

Pete Warden's blog

TinyML Book Screencast 4 – Quantization For the past few months I’ve been working with Zain Asgar and Keyi Zhang on EE292D, Machine Learning on Embedded Systems, at Stanford. We’re hoping to open sour...

Want to Learn Quantization in The Large Language Model?

Towards AI

A simple guide to teach you intuition about quantization with simple mathematical derivation and coding in PyTorch. 1\. Image by writer: Flow shows the need for quantization. (The happy face and angr...

Democratizing LLMs: 4-bit Quantization for Optimal LLM Inference

Towards Data Science

Quantizing a model is a technique that involves converting the precision of the numbers used in the model from a higher precision (like 32-bit floating point) to a lower precision (like 4-bit integers...