Abstract Vision Transformers (ViTs) have exhibited exceptional performance across diverse
computer vision tasks, while their substantial parameter size incurs significantly increased …

Post-training quantization (PTQ) for vision transformers (ViTs) has received increasing
attention from both academic and industrial communities due to its minimal data needs and …

Data-free quantization (DFQ), which facilitates model quantization without real data to
address increasing concerns about data security, has garnered significant attention within …

We reveal that low-bit quantization favors undertrained large language models (LLMs) by
observing that models with larger sizes or fewer training tokens experience less quantization …

The majority of quantization methods have been proposed to reduce the model size of
Vision Transformers, yet most of them have overlooked the quantization of non-linear …