Transformer models are a class of neural networks built to handle ordered data such as language, audio or code by relying entirely on attention mechanisms rather than recurrence or convolutions. Their core idea is to let each token in an input sequence directly attend to every other token, learning which positions matter most for a given task. This is implemented with multi head self attention, which projects tokens into several subspaces so that different heads can capture different types of relationships, such as syntax, long range dependencies or semantic similarity.

The original architecture processes inputs in parallel, which greatly improves training efficiency compared to recurrent networks. Positional encodings are added so that the model can still infer word order. Stacks of attention and feedforward layers progressively transform token representations, enabling the model to build hierarchical abstractions of text. During generation, transformers often use masked attention so each token only attends to previous ones, making them suitable for autoregressive language modeling.

Research has shown that scaling transformer models in data, parameters and computation yields strong empirical gains and can lead to emergent capabilities. Variants such as encoder only, decoder only and encoder decoder architectures target different applications, including classification, translation and open ended text generation. Further work has addressed efficiency and context length with sparse attention, low rank approximations and architectures that can handle very long sequences. Analysis of trained transformers has revealed interpretable circuits and neuron like features inside attention heads, suggesting that some internal mechanisms align with human understandable concepts. Overall, transformers have become the backbone of modern AI systems across many domains.