Master LLMs with our FREE course in collaboration with Activeloop & Intel Disruptor Initiative. Join now!

Publication

8-Bit LLM Quantization with Lightning Fabric
Latest   Machine Learning

8-Bit LLM Quantization with Lightning Fabric

Last Updated on May 12, 2024 by Editorial Team

Author(s): Tim Cvetko

Originally published on Towards AI.

2024 — Easiest Way to any LLM Int-8 Quantization with Lightning Fabric

LLMs are called “large” for a reason. Models, like GPT-4, have over 220B weights, and over 1.4T total parameters. For us mortals, fine-tuning LLMs that have otherwise performed well on general tasks must take some form of optimization:

Model distillation — training a comparatively-smaller LLMPEFT — freeze some layers during fine-tuningPruning — reducing model size after trainingQuantization — using less precise bits to store weight information

->💡 8-Bit Quantization(int8) enables loading larger models you normally wouldn’t be able to fit into memory, and speeding up inference.

8-bit Quantization: Image by AuthorP1: Introduction to Model QuantizationP2: Why 8-Bit QuantizationP3: How YOU can Fine-Tune any LLM with Lightning AI’s Fabric Module

Quantization is a must for most production systems given that edge devices and consumer-grade hardware typically require models of a much smaller memory footprint than more powerful hardware such as NVIDIA’s A100 80GB. Learning about this technique will enable a better understanding of deployment of LLMs like a Llama 2 and SDXL, and requirements for edge devices in robotics, vehicles, and other systems.

The size of a model is determined by the number of its parameters, and their precision, typically one of float32, float16 or bfloat16.

Float Precision: Image by Author

To calculate the model size in bytes,… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI

Feedback ↓