Model Distillation

A 7-part series: why frontier models are too expensive to deploy, the math that transfers knowledge into small ones, a hands-on build, and how to keep a distilled model good in production.

1. 01
   

   Why Everyone Runs a Model They Can't Afford

   The problem nobody budgets for

   7 min
2. 02
   

   Soft Labels: The Secret Ingredient

   Why wrong answers teach more than right ones

   7 min
3. 03
   

   The Three Flavors of Distillation: Response, Feature, and Relation

   Copy the answers, copy the thinking, or copy the worldview

   7 min
4. 04
   

   The Math That Makes It Work: KL Divergence and Temperature, Demystified

   One loss function, two teachers — and the scaling factor everyone forgets

   7 min
5. 05
   

   Hands-On: Distilling a Real Model (And Watching It Actually Work)

   Theory meets a GPU — shrink a model, keep the accuracy, publish the receipts

   5 min
6. 06
   

   Distillation in the Wild: DistilBERT, Mini Models, and Synthetic Data

   How the pros actually do it — and the gray zone nobody likes to discuss

   6 min
7. 07
   

   Production Distillation: Pipelines, Pitfalls, and What's Next

   Distilling once is a project. Staying distilled is a discipline.

   7 min