claudegoodies
Skill

hqq-quantization

From davila7

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.

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davila7/claude-code-templates
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Actively maintained
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Source file
cli-tool/components/skills/ai-research/optimization-hqq/SKILL.md

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# 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
- **Multiple backends**: PyTorch, ATEN, TorchAO, Marlin, BitBlas for optimized inference
- **Flexible precision**: 8/4/3/2/1-bit with configurable group sizes
- **Framework integration**: Native HuggingFace and vLLM support
- **PEFT compatible**: Fine-tune quantized models with LoRA

**Use alternatives instead:**
- **AWQ**: Need calibration-based accuracy, production serving
- **GPTQ**: Maximum accuracy with calibration data available
- **bitsandbytes**: Simple 8-bit/4-bit without custom backends
- **llama.cpp/GGUF**: CPU inference, Apple Silicon deployment

## Quick start

### Installation

```bash
pip install hqq

# With specific backend
pip install hqq[torch]      # PyTorch backend
pip install hqq[torchao]    # TorchAO int4 backend
pip install hqq[bitblas]    # BitBlas backend
pip install hqq[marlin]     # Marlin backend
```

###
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