hqq-quantization

# 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 ``` ### Basic quantization ```python from hqq.core.quantize import BaseQuantizeConfig, HQQLinear import tor...