Model Families

In the context of large language models (LLMs), a "model family" refers to a group of related models that share a similar underlying architecture and training methodology, often developed by the same research team, with variations in size, capabilities, and specific applications within that family; essentially, different versions of the same core model design, like the "GPT" family which includes GPT-3, GPT-3.5, and GPT-4, all sharing similar features but with varying levels of performance. 

Key points about model families:

• Common origin:

  Models within a family usually come from the same research group, using a similar base architecture and training process. 

• Variations in size and capabilities:

  While sharing a core design, different models within a family can have varying sizes (number of parameters) and be optimized for different tasks, like text generation, translation, or question answering. 

• Examples of LLM families:

  • GPT (Generative Pre-trained Transformer): Includes GPT-2, GPT-3, GPT-3.5, GPT-4 

  • BERT (Bidirectional Encoder Representations from Transformers): Used for tasks like sentiment analysis and text classification 

  • PaLM (Pathways Language Model): Google's family of advanced LLMs 

  • LaMDA (Language Model for Dialogue Applications): Google's dialogue-focused LLM