Performance Optimization After Fine-Tuning
Performance Optimization After Fine-Tuning
After fine-tuning, optimization becomes critical.
1) Quantization
Reduce model precision to lower memory usage.
2) Pruning
Remove less important parameters.
3) Inference Acceleration
- ONNX conversion
- TensorRT optimization
- GPU acceleration
4) Enterprise Impact
Optimization reduces infrastructure cost and improves latency.
5) Summary
Optimization ensures your customized model is production-ready.
