Decoder-only models represent a powerful architecture in the transformer family, focusing exclusively on the decoder component. These models, exemplified by GPT (Generative Pre-trained Transformer), Claude, and Llama, have revolutionized text generation capabilities.
Key Characteristics
Autoregressive Generation: Generates text one token at a time, with each new token conditioned on previously generated tokens
Unidirectional Attention: Each position can only attend to previous positions in the sequence (causal attention)
Generative Focus: Optimized for text generation rather than understanding or representation
Scaling Properties: Performance tends to scale well with model size and training data
Popular Models
GPT Family: OpenAI's GPT models (GPT-3, GPT-4) represent the state-of-the-art in large language models
LLaMA: Meta's open-source large language model that has enabled a wave of innovation through fine-tuning
Claude: Anthropic's assistant models designed with a focus on helpfulness, harmlessness, and honesty
Falcon: Technology Innovation Institute's open-source models trained on massive datasets
Build This Pattern
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Explain decoder-only (GPT-style) transformer architecture. Architecture: describe the stack of decoder blocks, each containing causal self-attention with triangular masking, feed-forward network, residual connections, and layer normalization. Show how autoregressive generation produces one token at a time with KV-cache optimization for efficiency. Contrast with encoder-only and encoder-decoder architectures. Cover scaling laws and how performance improves predictably with model size, data size, and compute. Discuss training objective (next token prediction) and inference techniques (temperature, top-k, top-p sampling). Reference representative model families. Error handling: discuss common generation failure modes - repetition loops, degeneration (model collapses to high-probability tokens), and context forgetting in long generations. Cover mitigation: repetition penalty, frequency penalty, contrastive search, sliding window attention. Edge cases: discuss handling special tokens, out-of-distribution inputs like adversarial prompts, and very short or very long generation requests. Best practices: include practical guidance on choosing sampling parameters for different tasks, implementing efficient batching for inference, and using logit processing techniques (repetition penalty, min-p sampling). Testing: suggest evaluating generation quality with perplexity on held-out data, measuring diversity with distinct-N metrics, and stress-testing with adversarial prompts.