Quantization
Calibration data is a sample of model inputs used to derive quantization scales or protection choices. Its activation patterns stand in for the workload the quantized model will serve.
Unrepresentative samples can hide important ranges or overprotect patterns that rarely occur. Calibration quality therefore bounds how honestly post-training choices reflect production inputs.
Teams sample relevant tasks, prompt styles, and sequence shapes without using evaluation answers as tuning targets. They keep a separate evaluation set for quality acceptance after quantization.
Calibration data is a sample of model inputs used to derive quantization scales or protection choices. Its activation patterns stand in for the workload the quantized model will serve. Teams sample relevant tasks, prompt styles, and sequence shapes without using evaluation answers as tuning targets. They keep a separate evaluation set for quality acceptance after quantization.
Unrepresentative samples can hide important ranges or overprotect patterns that rarely occur. Calibration quality therefore bounds how honestly post-training choices reflect production inputs.
RunInfraby RightNow© 2026 RunInfra. All rights reserved.
