Run any Huggingface model locally

A guide/colab notebook to quantize LLMs in GGUF formate to run them locally

Code Updated : 29th December 2023

Introduction

In the ever-evolving landscape of natural language processing, language models are at the forefront, playing a central role in comprehending and generating human-like text. The increasing complexity and size of these models present a critical challenge: the need for efficiency without compromising performance. As language models continue to advance, striking the right balance becomes essential for their effective deployment across various applications.

This is where quantization steps in as a crucial technique.

Need for Quantization

As neural network models typically operate with 32-bit floating point weights (float32), consuming 4 bytes or 32 bits of memory for each value, quantization becomes a necessity. By reducing this precision through techniques like INT8 quantization, where weights are represented using 8-bit integers instead of the original 32-bit floats, not only does the model size decrease significantly, but computational speed also sees a notable boost. Specialized 8-bit matrix multiplication kernels in modern GPUs and TPUs further optimize the performance of INT8 quantization.