LAC

Learnable Activation Clipping (LAC) is a differentiable activation-range optimization technique for neural network quantization, especially for LLMs. It introduces channel-scaling structural transformations on top of learnable clipping to boost the accuracy of low-bit W/A quantization. For details about the algorithm, see OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models.