What Is a Transformer Model?

A transformer model is a type of neural network architecture that uses a mechanism called attention to weigh the relationships between all parts of its input at once. Introduced in 2017, it is the architecture behind most modern large language models and much of today's generative AI.

How a transformer model works

The key idea in a transformer is attention, which lets the model consider how every part of the input relates to every other part when processing it. Reading a sentence, it can connect a pronoun to the noun it refers to several words back, weighing which other words matter most for understanding each one. This gives it a strong grasp of context.

Attention also lets a transformer process an entire input in parallel rather than strictly one item at a time, which older sequence architectures could not do. That parallelism made it practical to train very large models on very large datasets, which is a major reason transformers, and the models built on them, scaled up so quickly.

Why transformer models matter for AI

Transformer models matter because they are the foundation of the current wave of AI. Large language models, many image and audio models, and most generative AI are built on the transformer architecture, and its ability to handle context well and train efficiently at scale is much of why recent progress happened. Understanding that these systems are transformers underneath explains a lot about how they behave. At Custom AI Studio, the models we build on are transformer-based, and we adapt them to a client's specific data and goals.