hqq-quantization

You can quantize large language models without calibration data by applying calibration-free 4/3/2-bit quantization to reduce memory footprint and inference latency. This approach skips data preparation and directly compresses pretrained weights for fast deployment.

Calibration-free quantization compresses large language model weights into 4/3/2-bit formats without requiring representative datasets to tune activations. You need it to reduce memory footprint and inference latency when you lack domain-specific calibration data.

Yes, calibration-free quantization works with HuggingFace Transformers and vLLM deployments natively. It provides flexible backends and mixed-precision configurations to enable fast, memory-efficient inference across these supported platforms.

Yes, you can use PEFT and LoRA workflows with calibration-free LLM quantization. It offers PEFT and LoRA compatibility and integrates easily into existing transformer workflows for efficient fine-tuning on quantized models.

Calibration-free LLM quantization supports 8/4/3/2/1-bit backends for flexible mixed-precision configurations. These bit-width options allow you to balance memory savings and inference latency based on your deployment requirements.

Use calibration-free quantization to skip the time-consuming dataset preparation phase required by standard quantization. It directly reduces memory footprint and inference latency across HuggingFace and vLLM platforms without needing representative calibration batches.