hqq-quantization

You can quantize LLM weights without calibration data by applying calibration-free 4-, 3-, 2-, or 1-bit configurations to your pretrained model. This approach enables fast, memory-efficient inference and deployment without needing a calibration dataset.

Yes, calibration-free quantization supports HuggingFace Transformers integration and is suitable for vLLM deployments. You can configure mixed-precision workflows to save memory and improve inference throughput across these platforms.

Yes, 4-bit LLM quantization requires Python with hqq>=0.2.0 and torch>=2.0.0. You can also utilize optional backends such as marlin, torchao_int4, or bitblas to optimize the quantization and inference process.

The best way to deploy a mixed-precision quantized LLM is by configuring a quantization config on your pretrained model and using compatible backends like Marlin or BitBlas. This allows you to save memory and improve inference throughput during deployment.

Calibration-free LLM quantization supports 4-bit, 3-bit, 2-bit, and 1-bit configurations. These options allow you to flexibly reduce model memory requirements based on your specific hardware and accuracy needs.