Welcome to the technical documentation for GPT (Generative Pre-trained Transformer) models. This page provides insights into the architecture, training process, and applications of GPT.

What is GPT?

GPT is a series of large language models developed by OpenAI, based on the transformer architecture. These models are pre-trained on vast amounts of text data to understand and generate human-like language.

Key Features

  • Transformer Architecture: Utilizes self-attention mechanisms for efficient parallel processing.
  • Pre-training: Trained on diverse text corpora to capture general knowledge.
  • Fine-tuning: Adapted for specific tasks like text completion or classification.

Technical Components

  1. Attention Mechanism

    • Enables the model to focus on relevant parts of the input.
    • 📌 View GPT_Architecture for a detailed diagram.

  2. Layered Structure

    • Composed of multiple transformer layers for hierarchical processing.
    • 🔍 Explore Transformer_Model for deeper technical analysis.

  3. Training Data

    • Sources include books, articles, and web texts.
    • 📚 Check Data_Sources for more information.

Applications

  • Natural Language Understanding: Question answering, sentiment analysis.
  • Text Generation: Writing, coding, and creative content creation.
  • Dialogue Systems: Chatbots and virtual assistants.

Expand Reading

For advanced topics on GPT optimization and deployment, visit /en/nlp/models/gpt/advanced.

GPT_Technical_Documentation