A GPT model is a decoder-only transformer for generating coherent, contextually relevant text from an input sequence. GPT stands for Generative Pre-Trained Transformer; these models train on large text collections and generate text token by token without a separate encoder stack. section 9.6.1 section 9.12
A practical GPT-style decoder combines several familiar neural-network components:
- Multiheaded attention projects queries, keys, and values through parallel heads, allowing different heads to capture different relationships in a sequence. section 9.6.1
- Residual connections, also called skip connections, provide direct paths for gradients through deep networks. section 9.6.1
- Layer normalization, a multilayer perceptron, token or character embeddings, positional embeddings, and a final projection produce vocabulary scores. section 9.6.1
A small character-level GPT-style model
A compact PyTorch implementation embeds input characters and positions, passes them through six transformer blocks, and projects the resulting representations to vocabulary scores. Training this model on names can produce outputs such as “journe,” “green,” “huxson,” and “robinna.”
