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 fo…