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...
A lot has happened in transformer quantization over the past few years, from barely being able to quantize a 7B model in INT8 without destroying accuracy, to routinely fitting a 70B model in 4-bits on a single GPU. But existing guides on the...
Machine Learning (ML) has witnessed a surge in interest in recent years driven by the availability of large-scale datasets, advances in ML frameworks such as PyTorch and TensorFlow, rise of hardware accelerators (e.g., GPUs and TPUs) that enable...
In this case study, we explore compressing neural networks for efficient deployment on edge devices with limited resources. We explore practical techniques like quantization, pruning, and tensorization using off-the-shelf open-source tools. Our...