Smaller models, distillation, and cascades

Picture it

Imagine a busy hospital where a triage nurse greets every incoming patient. The nurse quickly patches up simple scrapes, but immediately routes complex cases to specialized surgeons. By directing easy questions to a small, nimble model and reserving the massive, slow model only for deep reasoning, we avoid wasting our heaviest machinery on lightweight tasks. This cascade keeps the whole system flowing smoothly and efficiently.

What to understand

• Quality/cost Pareto frontier, distillation, task specialization
• Confidence calibration, escalation, fallback, and model-version contracts

Follow the references

1. 1 · Paper

   Hinton et al. · Distilling the Knowledge in a Neural Network ↗

   Read the teacher/student setup. Ask which behavior the student learns and where task-specific evaluation is still required.

2. 2 · Code + guide

   EleutherAI · Language Model Evaluation Harness ↗

   Build task slices that reveal where the smaller model fails.

Make it concrete

Compare one strong model with a small-model cascade. Report success rate and total cost including router calls, retries, and escalations.