Category:
Category:
Model Distillation
Category:
Model Optimization
Definition
Compressing a large model into a smaller one while preserving performance.
Explanation
Distillation trains a smaller “student” model to mimic a larger “teacher” model. It reduces inference cost, improves speed, and enables on-device or enterprise deployment. Distilled models are ideal for routing systems, agent sub-tasks, and latency-sensitive applications.
Technical Architecture
Teacher Model → Knowledge Transfer → Student Model → Deployment
Core Component
Teacher LLM, student LLM, distillation dataset, evaluation suite
Use Cases
Edge AI, offline AI, fast agents, routing workflows
Pitfalls
Loss of reasoning ability; degraded accuracy for complex tasks.
LLM Keywords
Model Distillation, Compressed Llm, Student Teacher Models
Related Concepts
Related Frameworks
• Model Compression
• Routing Models
• MoE
• Distillation Workflow