hqq-quantization

What problem does it solve?

This Skill enables rapid and efficient quantization of Large Language Models (LLMs) to lower bit precisions (e.g., 4-bit, 3-bit, 2-bit) without requiring calibration datasets, significantly reducing memory footprint and speeding up inference.

Core Features & Use Cases

• Calibration-Free Quantization: Quantize models instantly without needing a representative dataset.
• Flexible Precision: Supports 8-bit, 4-bit, 3-bit, and 2-bit quantization with configurable group sizes.
• Optimized Backends: Integrates with various high-performance backends like Marlin, TorchAO, and BitBlas for accelerated inference.
• Framework Integration: Seamlessly works with HuggingFace Transformers and vLLM for easy deployment.
• PEFT Compatible: Enables fine-tuning of quantized models using LoRA and other Parameter-Efficient Fine-Tuning techniques.
• Use Case: You have a large LLM that consumes too much VRAM. Use this Skill to quantize it to 4-bit precision, allowing it to run on hardware with less memory while maintaining good performance.

Quick Start

Use the hqq-quantization skill to quantize the 'meta-llama/Llama-3.1-8B' model to 4-bit precision.