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Chapter 67: Model Merging & Optimization

Blend strengths and shrink costs. This chapter builds a model-merging skill to combine checkpoints, apply adapters, and optimize performance/latency for your Task API use cases.

Goals​

  • Understand when merging beats retraining
  • Apply adapter/LoRA merges and safety checks
  • Evaluate merged models for quality and regressions
  • Optimize latency/cost with quantization and runtime tuning
  • Capture repeatable merge/optimize steps in a skill

Lesson Progression​

  • Build the model-merging skill
  • Merging strategies and tooling
  • Quality/safety evaluation of merged models
  • Optimization (quantization, runtime tweaks)
  • Capstone: merged/optimized model for Task API; finalize the skill

Outcome & Method​

You finish with a merged/optimized model tuned for your workload and a reusable model-merging skill.

Prerequisites​

  • Chapters 63-66 (data, SFT, persona, function calling)