Pruning

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Pruning is a model compression technique that reduces the size of a neural network by removing parts that don’t contribute much to its predictions. This can mean setting tiny weights to zero or removing whole neurons or filters so the model uses fewer operations and less memory. A typical workflow is to train a full model first, identify which weights or structures are least important, remove them, and then fine-tune the model to regain lost accuracy.

Pruning is popular for deploying large models on devices with limited resources or for reducing cloud compute costs. When done carefully, it can significantly shrink a model and speed up inference with little impact on quality. Many teams also combine pruning with quantization or knowledge distillation for even greater efficiency.

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