Transformer GPT

Transformer GPT (Generative Pre-trained Transformer) is a family of autoregressive language models built on decoder-only transformer architecture, designed for text generation through next-token prediction. Unlike encoder-decoder transformers, GPT models use only the decoder stack with masked self-attention to prevent information leakage from future tokens during training. The architecture employs multi-head attention mechanisms, feed-forward networks, and positional embeddings to process sequential text data. GPT models undergo unsupervised pre-training on vast text corpora using causal language modeling objectives, learning to predict subsequent words based on preceding context. Key innovations include scaling to billions of parameters, in-context learning capabilities, and emergence of complex reasoning abilities. The GPT series demonstrates how transformer architecture can achieve remarkable language understanding and generation through scale and architectural refinements. For AI agents, transformer GPT models serve as powerful reasoning engines enabling natural language understanding, instruction following, and complex task completion.