Model Quantization and Optimization

Optimize my trained model for efficient inference.

Model details:
- Model type: [CNN/TRANSFORMER/etc.]
- Framework: [PyTorch/TensorFlow]
- Current size: [PARAMETERS/MB]
- Target device: [GPU/CPU/MOBILE/EDGE]

Optimization requirements:
1. Quantization:
   - Post-training quantization (INT8)
   - Quantization-aware training
   - Mixed precision inference
2. Pruning:
   - Magnitude pruning
   - Structured pruning
   - Iterative pruning
3. Knowledge distillation:
   - Teacher-student setup
   - Distillation losses
   - Feature matching
4. Architecture optimization:
   - Operator fusion
   - Memory layout optimization
   - Graph optimization
5. Export formats:
   - ONNX
   - TensorRT
   - TFLite
   - CoreML
6. Benchmarking:
   - Latency measurement
   - Throughput testing
   - Accuracy degradation
7. Deployment:
   - Runtime selection
   - Batching optimization
   - Hardware-specific tuning

Balance speed vs. accuracy tradeoffs.