The development of Generative Pre-trained Transformer (GPT) language models by OpenAI has revolutionized the field of natural language processing (NLP). As the models evolved from GPT to GPT-4, their complexity and capabilities have increased significantly. In this post, we will take a deep dive into the parameters of the GPT series, examining the growth and implications of these powerful language models.

Topic touched upon:

  • The Transformer Architecture
  • GPT: The Beginning
  • GPT-2: Doubling Down on Parameters
  • GPT-3: The Parameter Giant
  • GPT-4: The Mysterious Future
  • ChatGPT: Conversational AI at its Best
  • GPT-4 Implications: Opportunities and Challenges

The Transformer Architecture

The GPT series is built on the Transformer architecture, which has become the go-to model for NLP tasks. Transformer models consist of layers that include self-attention and feed-forward elements, with each layer having its own set of parameters. The number of parameters in a Transformer model is crucial to its performance and is calculated by multiplying several factors:

This post is for subscribers only

Sign up now to read the post and get access to the full library of posts for subscribers only.

Sign up now Already have an account? Sign in