Decoder-Only Model

Decoder-Only Model is a neural network architecture that uses only the decoder component of the traditional encoder-decoder framework, generating sequences through autoregressive prediction where each token is predicted based on all previously generated tokens. This streamlined design eliminates the encoder entirely, relying on self-attention mechanisms to process input context and generate coherent output sequences.

Decoder-only models excel at text generation tasks by conditioning on prompt inputs and iteratively predicting subsequent tokens using causal masking to prevent information leakage from future positions. The architecture powers most modern large language models including GPT series, demonstrating superior performance in conversational AI, creative writing, and code generation applications. Advanced implementations incorporate techniques like rotary positional embeddings, layer normalization optimizations, and efficient attention patterns to scale effectively across billions of parameters while maintaining training stability and inference speed.