Back to Architecture Recipes

Decoder-only Models

GPT-styleGeneration

GPT-style

GPT-style architectures focused on text generation.

Overview

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

Core steps

  1. Input Processing: Text is tokenized into discrete tokens and converted to embeddings
  2. Positional Encoding: Position information is added to token embeddings to preserve sequence order
  3. Decoder Blocks: Multiple layers of masked self-attention and feed-forward networks process the embeddings
  4. Masked Self-Attention: Each position can only attend to previous positions, enforcing the autoregressive property
  5. Output Layer: Final representations are projected to vocabulary-sized logits and converted to probabilities
  6. Token Generation: The model samples the next token based on output probabilities and adds it to the sequence

Inference optimization

  • KV caching: Avoid recomputing attention for prefix
  • Batched inference: Process multiple requests together
  • Speculative decoding: Use smaller model for draft
  • Quantization: Reduce precision for efficiency

Architectural variants

  • Causal LM: Standard autoregressive generation
  • Prefix LM: Bidirectional prefix, causal generation
  • GLM-style: Masked spans in autoregressive framework

Architecture overview

Decoder-only models use masked self-attention to process sequences. Each token can only attend to previous tokens, making them ideal for text generation tasks.

Core steps

  • Input Processing: Text is tokenized into discrete tokens and converted to embeddings
  • Positional Encoding: Position information is added to token embeddings to preserve sequence order
  • Decoder Blocks: Multiple layers of masked self-attention and feed-forward networks process the embeddings
  • Masked Self-Attention: Each position can only attend to previous positions, enforcing the autoregressive property
  • Output Layer: Final representations are projected to vocabulary-sized logits and converted to probabilities
  • Token Generation: The model samples the next token based on output probabilities and adds it to the sequence

Applications

Text Generation

  • Creative Writing: Stories, poems, scripts, and other creative content
  • Content Creation: Blog posts, marketing copy, and product descriptions
  • Code Generation: Writing and explaining programming code across languages

Conversational AI

  • Chatbots: Human-like conversational agents for customer service
  • Virtual Assistants: Task-oriented helpers for productivity and information retrieval
  • Role-playing: Characters with specific personalities or expertise

Text Transformation

  • Summarization: Condensing long documents into concise summaries
  • Paraphrasing: Rewriting text while preserving meaning
  • Translation: Converting text between languages
  • Style Transfer: Changing the tone, formality, or style of text

Reasoning and Problem Solving

  • Question Answering: Providing factual responses to queries
  • Mathematical Reasoning: Solving math problems step by step
  • Logical Deduction: Drawing conclusions from premises
  • Planning: Breaking down complex tasks into actionable steps

Industry applications

  • Healthcare: Medical documentation assistance, patient communication, and research literature analysis
  • Legal: Contract analysis, legal research, document drafting, and case summarization
  • Education: Personalized tutoring, content creation, and automated feedback on assignments
  • Finance: Financial report analysis, market research summaries, and customer service automation
  • Software Development: Code generation, debugging assistance, documentation writing, and code explanation
  • Creative Industries: Content creation, ideation, scriptwriting, and collaborative storytelling
Code View

Decoder-only Models Implementation

// Decoder-only Models recipe using OpenAI
// Install: bun add openai

import OpenAI from "openai";

async function main() {
  const input = "Add your prompt here.";
  const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });
  const system = "You are a senior AI engineer and technical writer. Explain how the architecture applies to the request and outline practical implementation guidance. Recipe: Decoder-only Models. Description: GPT-style architectures focused on text generation. Focus: GPT-style Provide actionable, implementation-ready guidance.";
  const user = `Request: ${input}`;

  const openaiResponse = await openai.chat.completions.create({
    model: "gpt-4o-mini",
    messages: [
      { role: "system", content: system },
      { role: "user", content: user },
    ],
  });

  const openaiText = openaiResponse.choices[0]?.message?.content?.trim() ?? "";

  console.log(openaiText);
}

main().catch((error) => {
  console.error(error);
  process.exitCode = 1;
});