#4326 Distilling a T-param LLM to Fine-Tune a Smaller LLM
#4326 Distilling a T-param LLM to Fine-Tune a Smaller LLM #4326 If I wanted to distill everything a Trillion-parameter LLM "knows" about a subject, for example a bicycle, what are the major categories of information to retrieve (for the bicycle, or any subject)? To distill the vast, multidimensional "knowledge" of a massive model like a trillion-parameter LLM, you have to move beyond a simple encyclopedia entry. These models don't just store facts; they store relational patterns, functional logic, and cultural contexts. If you were to "drain" the model of everything it knows about a bicycle—or any object—you would organize that data into these five major categories: 1. Physical & Structural Ontology This is the "blueprints" level. It covers what the object is made of and how it occupies space. Taxonomy: What is it? (A human-powered, pedal-driven, single-track vehicle). Anatomy: A breakdown of components (frame, drivetrain, derailleurs,...