Transformer architecture is a neural network design built around attention layers, feed-forward layers, and positional information rather than recurrence or convolution as the main sequence mechanism. It enables efficient parallel training and has become a core design for large language models and other foundation models.
Transformer architecture is a type of deep learning model designed to process sequences of data, eg text, by focusing on the relationship between different parts of the input. The transformers are fast, flexible, easily scalable and are highly efficient in understanding context, making them the foundation for generative systems.