model accuracy reduction
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Quantization introduces approximation error.
Some layers and activations are more sensitive than others. Without calibration, reduced precision distorts learned representations.
Use quantization-aware training or selectively exclude sensitive layers.
Common mistakes: Post-training quantization without evaluation, quantizing embeddings blindly and ignoring task sensitivity
Compression always trades something.