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diff --git a/openvino/openvino_model_qint8_quantized.xml b/openvino/openvino_model_qint8_quantized.xml
new file mode 100644
index 0000000..7152d19
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+++ b/openvino/openvino_model_qint8_quantized.xml
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+<?xml version="1.0"?>
+<net name="Model0" version="11">
+	<layers>
+		<layer id="2" name="input_ids" type="Parameter" version="opset1">
+			<data shape="?,?" element_type="i64" />
+			<output>
+				<port id="0" precision="I64" names="input_ids">
+					<dim>-1</dim>
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="1" name="attention_mask" type="Parameter" version="opset1">
+			<data shape="?,?" element_type="i64" />
+			<output>
+				<port id="0" precision="I64" names="attention_mask">
+					<dim>-1</dim>
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="0" name="token_type_ids" type="Parameter" version="opset1">
+			<data shape="?,?" element_type="i64" />
+			<output>
+				<port id="0" precision="I64" names="token_type_ids">
+					<dim>-1</dim>
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="3" name="Constant_204147" type="Const" version="opset1">
+			<data element_type="i8" shape="30522, 384" offset="0" size="11720448" />
+			<output>
+				<port id="0" precision="I8">
+					<dim>30522</dim>
+					<dim>384</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="4" name="Convert_204148" type="Convert" version="opset1">
+			<data destination_type="f32" />
+			<input>
+				<port id="0" precision="I8">
+					<dim>30522</dim>
+					<dim>384</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32">
+					<dim>30522</dim>
+					<dim>384</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="5" name="Constant_204149" type="Const" version="opset1">
+			<data element_type="f32" shape="30522, 1" offset="11720448" size="122088" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>30522</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="6" name="__module.embeddings.word_embeddings/aten::embedding/Gather/fq_weights_0" type="Multiply" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>30522</dim>
+					<dim>384</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>30522</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>30522</dim>
+					<dim>384</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="7" name="__module.embeddings.word_embeddings/aten::embedding/Convert" type="Convert" version="opset1">
+			<data destination_type="i32" />
+			<input>
+				<port id="0" precision="I64">
+					<dim>-1</dim>
+					<dim>-1</dim>
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+			<output>
+				<port id="1" precision="I32">
+					<dim>-1</dim>
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="8" name="__module.embeddings.word_embeddings/aten::embedding/Constant" type="Const" version="opset1">
+			<data element_type="i32" shape="" offset="11842536" size="4" />
+			<output>
+				<port id="0" precision="I32" />
+			</output>
+		</layer>
+		<layer id="9" name="__module.embeddings.word_embeddings/aten::embedding/Gather" type="Gather" version="opset8">
+			<data batch_dims="0" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>30522</dim>
+					<dim>384</dim>
+				</port>
+				<port id="1" precision="I32">
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+					<dim>-1</dim>
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+				<port id="2" precision="I32" />
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+			<output>
+				<port id="3" precision="FP32" names="79,inputs_embeds">
+					<dim>-1</dim>
+					<dim>-1</dim>
+					<dim>384</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="10" name="Constant_204151" type="Const" version="opset1">
+			<data element_type="i8" shape="2, 384" offset="11842540" size="768" />
+			<output>
+				<port id="0" precision="I8">
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+			</output>
+		</layer>
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+			<data destination_type="f32" />
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+				<port id="0" precision="I8">
+					<dim>2</dim>
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+			<output>
+				<port id="1" precision="FP32">
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+		</layer>
+		<layer id="12" name="Constant_204153" type="Const" version="opset1">
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+		</layer>
+		<layer id="13" name="__module.embeddings.token_type_embeddings/aten::embedding/Gather/fq_weights_0" type="Multiply" version="opset1">
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+			<input>
+				<port id="0" precision="FP32">
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+					<dim>384</dim>
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+				<port id="1" precision="FP32">
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+			<output>
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+			</output>
+		</layer>
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+			</output>
+		</layer>
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+			<output>
+				<port id="0" precision="I32" />
+			</output>
+		</layer>
+		<layer id="16" name="__module.embeddings.token_type_embeddings/aten::embedding/Gather" type="Gather" version="opset8">
+			<data batch_dims="0" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>2</dim>
+					<dim>384</dim>
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+					<dim>-1</dim>
+					<dim>-1</dim>
+					<dim>384</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="17" name="__module.embeddings/aten::add/Add" type="Add" version="opset1">
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+			<input>
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+					<dim>-1</dim>
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+					<dim>512</dim>
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+			<framework value="pytorch" />
+			<is_python_object value="True" />
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+			<friendly_names_were_updated value="True" />
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+				<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']" />
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+				<model_type value="transformer" />
+				<preset value="mixed" />
+				<subset_size value="300" />
+				<target_device value="ANY" />
+			</quantization>
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+			<transformers_version value="4.43.4" />
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+</net>