  Transformer-based models have gained widespread popularity in both the
computer vision (CV) and natural language processing (NLP) fields. However,
significant challenges arise during post-training linear quantization, leading
to noticeable reductions in inference accuracy. Our study focuses on uncovering
the underlying causes of these accuracy drops and proposing a
quantization-friendly fine-tuning method, \textbf{QuantTune}. Firstly, our
analysis revealed that, on average, 65\% of quantization errors result from the
precision loss incurred by the dynamic range amplification effect of outliers
across the target Transformer-based models. Secondly, \textbf{QuantTune}
adjusts weights based on the deviation of outlier activations and effectively
constrains the dynamic ranges of the problematic activations. As a result, it
successfully mitigates the negative impact of outliers on the inference
accuracy of quantized models. Lastly, \textbf{QuantTune} can be seamlessly
integrated into the back-propagation pass in the fine-tuning process without
requiring extra complexity in inference software and hardware design. Our
approach showcases significant improvements in post-training quantization
across a range of Transformer-based models, including ViT, Bert-base, and OPT.
QuantTune reduces accuracy drops by 12.09\% at 8-bit quantization and 33.8\% at
7-bit compared to top calibration methods, outperforming state-of-the-art
solutions by over 18.84\% across ViT models.
