Transformers that Transform Well Enough to Support Near-Shallow Architectures - Stanford CS25 Lecture

This talk introduces precision language models, which use specialized self-attention architectures and non-random parameter initialization to train language models efficiently with limited resources. It also describes a developing application that runs these models on microprocessors as controllers for small electronic devices.