What is Model Distillation
Model Distillation - Compressing large models into smaller efficient ones
A technique for transferring knowledge from a large teacher model to a smaller student model, producing a compact model that approximates the teacher's performance. Model distillation is key to deploying multimodal AI models in production with low latency and cost.
How It Works
The teacher model (large, accurate) generates soft predictions (probability distributions over classes or continuous outputs) for training data. The student model (small, fast) is trained to match these soft predictions rather than hard labels. Soft predictions contain richer information about inter-class relationships than hard labels, enabling the student to learn the teacher's reasoning patterns in a compressed form.
Technical Details
Knowledge distillation uses a temperature-scaled softmax where higher temperatures produce softer probability distributions. The student loss combines the distillation loss (KL divergence from teacher outputs) with the standard task loss (cross-entropy with true labels). Feature distillation transfers intermediate representations rather than just final outputs. Common compressions achieve 4-10x size reduction with less than 5% accuracy loss.
Best Practices
- Distill from the largest available teacher model for maximum knowledge transfer
- Balance distillation loss and task loss weights to get the best of both signals
- Use a diverse and representative dataset for distillation, not just the original training set
- Evaluate the student model on the same benchmarks as the teacher to measure knowledge retention
Common Pitfalls
- Using a student architecture that is too small to capture the teacher's knowledge
- Distilling with too few examples, which does not capture the full range of teacher behavior
- Not tuning the temperature parameter, which controls how much soft label information is transferred
- Expecting the student to match the teacher exactly on all metrics
Advanced Tips
- Distill multimodal models to create efficient cross-modal encoders for production retrieval
- Use online distillation where teacher and student train simultaneously and learn from each other
- Apply task-specific distillation focused on the retrieval or classification task rather than general knowledge
- Combine distillation with quantization and pruning for maximum model compression