SKILL.md
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