Quantization and Distillation Deep Dive
Master advanced model compression: quantization-aware training, mixed precision, and distillation strategies for production deployment.
TL;DR
Advanced compression combines quantization-aware training, mixed-precision inference, and progressive distillation. Achieve 8x compression with <2% accuracy loss.
Quantization strategies
Post-training quantization (PTQ): Apply after training, simple but less accurate
Quantization-aware training (QAT): Simulate quantization during training, higher accuracy
Mixed precision: Different layers at different precisions
Distillation architectures
Feature distillation: Match intermediate layer outputs
Attention distillation: Transfer attention patterns
Data-free distillation: No original training data needed
Implementation best practices
- Calibrate on representative data
- Monitor per-layer sensitivity
- Fine-tune after quantization
- Validate on edge cases
What's next
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Key Terms Used in This Guide
Quantization
A compression technique that reduces AI model size and memory usage by using lower-precision numbers, making models faster and cheaper to run.
Model
The trained AI system that contains all the patterns it learned from data. Think of it as the 'brain' that makes predictions or decisions.
Training
The process of feeding data to an AI system so it learns patterns and improves its predictions over time.
AI (Artificial Intelligence)
Making machines perform tasks that typically require human intelligenceālike understanding language, recognizing patterns, or making decisions.
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