Transformer model

A transformer model is a type of machine learning architecture designed to handle sequential data, such as text or time series, more efficiently than older models like recurrent neural networks (RNNs). 

Transformers use an attention mechanism to weigh the importance of different parts of the input data. This allows them to understand context and relationships across long sequences, making them powerful for tasks such as translation, summarization, and answering questions.

During model training, transformers process information in parallel rather than step by step, which speeds up computation. They form the basis for many modern AI systems, including large language models (LLMs).