super-gradients/src/super_gradients/recipes/quantization_params/default_quantization_params.yaml

ptq_only: False              # whether to launch QAT, or leave PTQ only
selective_quantizer_params:
  calibrator_w: "max"        # calibrator type for weights, acceptable types are ["max", "histogram"]
  calibrator_i: "histogram"  # calibrator type for inputs acceptable types are ["max", "histogram"]
  per_channel: True          # per-channel quantization of weights, activations stay per-tensor by default
  learn_amax: False          # enable learnable amax in all TensorQuantizers using straight-through estimator
  skip_modules:              # optional list of module names (strings) to skip from quantization

calib_params:
  histogram_calib_method: "percentile"  # calibration method for all "histogram" calibrators, acceptable types are ["percentile", "entropy", mse"], "max" calibrators always use "max"
  percentile: 99.99                     # percentile for all histogram calibrators with method "percentile", other calibrators are not affected
  num_calib_batches:                    # number of batches to use for calibration, if None, 512 / batch_size will be used
  verbose: False                        # if calibrator should be verbose