Quantization

Quantization is a crucial technique in deep learning that involves converting a model’s parameters from high-precision formats, such as 32-bit floating point (FP32), to lower-precision formats like 8-bit integers (INT8). This process significantly reduces the model’s size and memory requirements, making it more efficient for deployment on various hardware platforms, including mobile devices. By sacrificing a small amount of numerical precision, quantization enables faster computations and lower power consumption, which is essential for running large AI models in resource-constrained environments. It plays a vital role in optimizing models for real-world applications without compromising their predictive accuracy.

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 ...

Using Quantized Models with Ollama for Application Development

MachineLearningMastery.com

Quantization is a frequently used strategy applied to production machine learning models, particularly large and complex ones, to make them lightweight by reducing the numerical precision of the model...

Quantization: How to Accelerate Big AI Models

Towards AI

In the world of deep learning, we are in an arms race for bigger, more powerful models. While this has led to incredible capabilities, it has also created a significant problem: these models are enorm...

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...

Dynamic Quantization

PyTorch Tutorials

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

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...