chapter one

1 Why rearchitecting LLMs matters

 

This chapter covers

  • Why generic LLMs don’t work
  • The model re-architects the pipeline as a solution.
  • Core techniques for building specialized models
  • A roadmap to architecting

Large language models (LLMs) are trained on large collections of text from many languages and subject areas. As a result, they can have hundreds of billions of parameters, sometimes approaching a trillion. They can handle many tasks, such as writing poetry, analyzing financial documents, generating code, and translating between languages. This breadth of knowledge is the source of their power, but it can also make them inefficient for specialized tasks, using more time and resources than necessary.

1.1 Current challenges to scaling LLMs

1.2 The solution: the model rearchitecting pipeline

1.3 Toolkit and techniques

1.4 Your LLM rearchitecture roadmap

1.5 Summary