Quantifier et distiller pour réduire le coût de l’inférence

A smaller model needs less memory and cheaper hardware to serve. Model compression trims size and cost while keeping most of the accuracy you worked for.

Quantization stores weights in fewer bits, like int8 instead of float32. The model gets roughly four times smaller and often runs faster on the same chip.

The simplest path quantizes an already-trained model with no retraining. Post-training quantization is one function call and a tiny accuracy hit.

Feeding a few real batches helps quantization pick good value ranges. This calibration step keeps accuracy higher than blind conversion alone.

When accuracy matters most, you simulate int8 math during training itself. Quantization-aware training costs more effort but recovers most lost accuracy.

Knowledge distillation trains a small student model to copy a big teacher. The student keeps much of the teacher's skill at a fraction of the cost.

The student learns from the teacher's full probability outputs, not just hard answers. These soft labels carry richer signal than a single class.

Distillation lets you swap a heavy model for a lean one, like DistilBERT for BERT. A smaller student means lower latency and a smaller serving instance.

Pruning removes weights that barely affect output, leaving a sparser, leaner network. It pairs well with both quantization and distillation.

Every shrink risks accuracy, so test the compressed model on real data. Watch the accuracy versus cost curve and stop before quality drops too far.

Compressed models pair nicely with fast runtimes like ONNX Runtime. Export once, then serve the smaller artifact on cheaper hardware.

Let us pin down what distillation actually does.

You quantized weights to fewer bits, distilled a small student from a big teacher, and pruned dead weights, all while watching the accuracy trade-off. 🪶