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		<conversion_parameters>
			<framework value="pytorch" />
			<is_python_object value="True" />
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		<nncf>
			<friendly_names_were_updated value="True" />
			<quantization>
				<advanced_parameters value="{'overflow_fix': 'disable', 'quantize_outputs': False, 'inplace_statistics': True, 'disable_channel_alignment': True, 'disable_bias_correction': False, 'batchwise_statistics': None, 'activations_quantization_params': None, 'weights_quantization_params': None, 'activations_range_estimator_params': {'min': {'statistics_type': None, 'aggregator_type': None, 'clipping_value': None, 'quantile_outlier_prob': 0.0001}, 'max': {'statistics_type': None, 'aggregator_type': None, 'clipping_value': None, 'quantile_outlier_prob': 0.0001}}, 'weights_range_estimator_params': {'min': {'statistics_type': None, 'aggregator_type': None, 'clipping_value': None, 'quantile_outlier_prob': 0.0001}, 'max': {'statistics_type': None, 'aggregator_type': None, 'clipping_value': None, 'quantile_outlier_prob': 0.0001}}, 'bias_correction_params': {'apply_for_all_nodes': False, 'threshold': None}, 'smooth_quant_alphas': {'convolution': -1, 'matmul': 0.95}, 'smooth_quant_alpha': None, 'backend_params': {}}" />
				<fast_bias_correction value="True" />
				<ignored_scope>
					<types value="['GroupNormalization']" />
				</ignored_scope>
				<model_type value="transformer" />
				<preset value="mixed" />
				<subset_size value="300" />
				<target_device value="ANY" />
			</quantization>
		</nncf>
		<optimum>
			<optimum_intel_version value="1.20.0.dev0+b31524c" />
			<optimum_version value="1.23.0" />
			<pytorch_version value="2.5.0.dev20240807+cu121" />
			<transformers_version value="4.43.4" />
		</optimum>
	</rt_info>
</net>