Generative transformer

Generative transformer is a neural network architecture that uses self-attention mechanisms to generate sequential data, primarily text, by predicting subsequent tokens based on preceding context. This decoder-only architecture employs masked self-attention to prevent information leakage from future positions during training, enabling autoregressive generation where each token depends on previously generated content. The model processes input through multiple transformer layers containing multi-head attention, feed-forward networks, and residual connections with layer normalization. Key innovations include positional encoding for sequence understanding, attention heads that capture different linguistic relationships, and parallel processing capabilities that enable efficient training on large corpora. Generative transformers power applications like text completion, dialogue systems, code generation, and creative writing. For AI agents, generative transformers serve as reasoning engines that produce coherent responses, generate plans, and communicate naturally with humans through structured language generation and instruction following.