quantization-and-model-compression

What problem does it solve?

This Skill addresses the challenge of shrinking and speeding up Large Language Models (LLMs) for efficient serving on lower-end hardware without the need for retraining.

Core Features & Use Cases

• Quantization: Reduces memory and cost by converting model weights and activations to lower-precision formats.
• Model Compression: Cuts down model size and improves latency through techniques like speculative decoding and distillation.
• Use Case: Ideal for optimizing LLMs for deployment on edge devices or servers with limited resources.

Quick Start

Use the quantization-and-model-compression skill to quantize the 'llama3-70b' model to INT4 precision with group size 128 and apply speculative decoding with a draft model 'small-llama'.