Half-Quadratic Quantization for LLMs without calibration data. Use when quantizing models to 4/3/2-bit precision without needing calibration datasets, for fast quantization workflows, or when deploying with vLLM or Hu…
---
name: hqq-quantization
description: Half-Quadratic Quantization for LLMs without calibration data. Use when quantizing models to 4/3/2-bit precision without needing calibration datasets, for fast quantization workflows, or when deploying with vLLM or HuggingFace Transformers.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Quantization, HQQ, Optimization, Memory Efficiency, Inference, Model Compression]
dependencies: [hqq>=0.2.0, torch>=2.0.0]
---
# HQQ - Half-Quadratic Quantization
Fast, calibration-free weight quantization supporting 8/4/3/2/1-bit precision with multiple optimized backends.
## When to use HQQ
**Use HQQ when:**
- Quantizing models without calibration data (no dataset needed)
- Need fast quantization (minutes vs hours for GPTQ/AWQ)
- Deploying with vLLM or HuggingFace Transformers
- Fine-tuning quantized models with LoRA/PEFT
- Experimenting with extreme quantization (2-bit, 1-bit)
**Key advantages:**
- **No calibration**: Quantize any model instantly without sample data… load the full skill through Skill Me