hqq-quantization

What problem does it solve?

This Skill addresses the challenge of large language model (LLM) memory and computational requirements by enabling efficient weight quantization, making LLMs faster and more accessible on limited hardware.

Core Features & Use Cases

• Calibration-Free Quantization: Quantize models to 4/3/2-bit precision without needing calibration datasets, significantly speeding up the quantization process.
• Optimized Backends: Supports various backends (PyTorch, ATEN, TorchAO, Marlin, BitBlas) for optimized inference performance across different hardware.
• Framework Integration: Seamlessly integrates with HuggingFace Transformers and vLLM for easy deployment and fine-tuning.
• Use Case: Deploy a large LLM like Llama-3.1-8B on a consumer GPU by quantizing it to 4-bit using HQQ, enabling faster response times and reduced memory footprint for your application.

Quick Start

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