Adaptive rounding techniques are one of the best ways to get more accuracy from quantization with the same bitwidth. In this series we’ll cover the evolution of adaptive rounding techniques starting from Adaround which delivers excellent quality...
In my previous blogpost, we discussed in detail the outlier problem in transformers. We realized that outliers are not just random noise - they encode important directional information that lets the model selectively skip updating token...
Modern Machine Learning models are trained with a large number of parameters, often too large, and this overparameterization is very useful during training as it creates a vast search space for the model to encode rich representations from data...