What is post-training optimization in AI models?

What is post-training optimization in AI models?

Post-training optimization refers to techniques applied after a model has been trained to improve its performance, efficiency, or deployment.

What are some common techniques used in post-training optimization?

Common techniques include quantization, pruning, knowledge distillation, and model compression.

How does quantization help in optimizing AI models?

Quantization reduces the precision of the model's weights, which decreases the model size and speeds up inference without significantly impacting accuracy.

Can you explain knowledge distillation?

Knowledge distillation involves training a smaller model (student) to replicate the behavior of a larger, pre-trained model (teacher), making it more efficient.