Transformer Models

Transformers use self-attention to determine how different elements of an input relate to one another, allowing the model to process information in parallel rather than strictly one sequence step at a time. They form the foundation of many modern language models and can be adapted for tasks involving text, images, audio, and other data.

Transformer models can efficiently learn complex relationships across large amounts of data and scale effectively to large model architectures. Their flexibility has made them central to modern natural language processing and generative AI, while their broad capabilities also make security, evaluation, and responsible deployment important.