Standard 14 stops to get here · leads to 1

Decoder-Only Model

A transformer architecture with only decoder layers, using causal masking for autoregressive generation (GPT family).

Your route here

14 stops · basics first
  1. Natural Language Processing ✓ understood

    The field of AI that lets computers read, interpret, translate and generate human language, from spam filters and search to chatbots.

  2. Token ✓ understood

    The basic unit of text that a language model processes, typically representing a word, subword, or character. Tokens are the fundamental building blocks for LLM input and output.

  3. Tokenization ✓ understood

    Splitting text into tokens, usually subword pieces, and mapping each to an integer ID so a language model can process it.

  4. Language Modeling ✓ understood

    Learning probability distributions over sequences of words to predict what comes next.

  5. Autoregressive Model ✓ understood

    A model that generates output one token at a time, using previously generated tokens as input for the next prediction.

  6. Dataset ✓ understood

    A collection of data examples used for training, validating, or testing machine learning models.

  7. Feature ✓ understood

    A single measurable property of an example, such as a house's floor area or how many links an email contains, used as an input to a model.

  8. Machine Learning ✓ understood

    Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.

  9. Neural Network ✓ understood

    A computational model inspired by biological neural networks, consisting of interconnected nodes (neurons) organized in layers that process information through weighted connections.

  10. Deep Learning ✓ understood

    A subset of machine learning that uses neural networks with multiple layers (deep neural networks) to learn hierarchical representations of data.

  11. Representation Learning ✓ understood

    Learning useful features or representations of data automatically, rather than hand-crafting them.

  12. Embedding ✓ understood

    A list of numbers (a vector) that represents a word, sentence, image or other item, learned so that similar items end up close together.

  13. Attention Mechanism ✓ understood

    A technique that lets a neural network weigh every part of its input when producing each output, focusing on the parts most relevant at that step.

  14. Transformer ✓ understood

    A neural network architecture, introduced in 2017, built from stacked self-attention and feed-forward layers; the basis of nearly every modern large language model.

  15. Decoder-Only Model · you are here ✓ understood

A transformer architecture with only decoder layers, using causal masking for autoregressive generation (GPT family).

This concept is essential for understanding large language models and forms a key part of modern AI systems.

  • GPT
  • Autoregressive
  • Transformer

Where it sits

Before this

Transformer Autoregressive Model
Decoder-Only Model

Leads to

GPT

Explore nearby

Language & LLMs GPT Generative Pre-trained Transformer - an autoregressive language model architecture that predicts the next token given previous context. Language & LLMs Encoder-Only Model A transformer with only encoder layers and bidirectional attention, suited for understanding tasks (BERT family). Language & LLMs Encoder-Decoder A architecture where the encoder processes input and the decoder generates output, used in translation and sequence-to-sequence tasks. Language & LLMs Causal Mask An attention mask ensuring tokens can only attend to previous positions, crucial for autoregressive generation.