diff --git "a/openvino_encoder_model.xml" "b/openvino_encoder_model.xml"
new file mode 100644--- /dev/null
+++ "b/openvino_encoder_model.xml"
@@ -0,0 +1,59663 @@
+<?xml version="1.0"?>
+<net name="Model0" version="11">
+	<layers>
+		<layer id="0" name="input_features" type="Parameter" version="opset1">
+			<data shape="?,80,3000" element_type="f32" />
+			<output>
+				<port id="0" precision="FP32" names="input_features">
+					<dim>-1</dim>
+					<dim>80</dim>
+					<dim>3000</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="1" name="self.conv1.weight" type="Const" version="opset1">
+			<data element_type="u8" shape="1280, 80, 3" offset="0" size="307200" />
+			<output>
+				<port id="0" precision="U8">
+					<dim>1280</dim>
+					<dim>80</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="2" name="Convert_38406" type="Convert" version="opset1">
+			<data destination_type="f16" />
+			<input>
+				<port id="0" precision="U8">
+					<dim>1280</dim>
+					<dim>80</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP16">
+					<dim>1280</dim>
+					<dim>80</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="3" name="self.conv1.weight/zero_point" type="Const" version="opset1">
+			<data element_type="u8" shape="1280, 1, 1" offset="307200" size="1280" />
+			<output>
+				<port id="0" precision="U8">
+					<dim>1280</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="4" name="Convert_38409" type="Convert" version="opset1">
+			<data destination_type="f16" />
+			<input>
+				<port id="0" precision="U8">
+					<dim>1280</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP16">
+					<dim>1280</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="5" name="self.conv1.weight/zero_point/subtract" type="Subtract" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1280</dim>
+					<dim>80</dim>
+					<dim>3</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1280</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1280</dim>
+					<dim>80</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="6" name="self.conv1.weight/scale" type="Const" version="opset1">
+			<data element_type="f16" shape="1280, 1, 1" offset="308480" size="2560" />
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1280</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="7" name="self.conv1.weight/fq_weights_1" type="Multiply" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1280</dim>
+					<dim>80</dim>
+					<dim>3</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1280</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1280</dim>
+					<dim>80</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="8" name="self.conv1.weight/fq_weights_1/convert" type="Convert" version="opset1">
+			<data destination_type="f32" />
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1280</dim>
+					<dim>80</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32">
+					<dim>1280</dim>
+					<dim>80</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="9" name="__module.conv1/aten::_convolution/Convolution" type="Convolution" version="opset1">
+			<data strides="1" dilations="1" pads_begin="1" pads_end="1" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>-1</dim>
+					<dim>80</dim>
+					<dim>3000</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1280</dim>
+					<dim>80</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>-1</dim>
+					<dim>1280</dim>
+					<dim>3000</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="10" name="__module.conv1/aten::_convolution/Reshape" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 1280, 1" offset="311040" size="5120" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>1280</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="11" name="__module.conv1/aten::_convolution/Add" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>-1</dim>
+					<dim>1280</dim>
+					<dim>3000</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>1280</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="131">
+					<dim>-1</dim>
+					<dim>1280</dim>
+					<dim>3000</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="12" name="aten::gelu/Gelu" type="Gelu" version="opset7">
+			<data approximation_mode="ERF" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>-1</dim>
+					<dim>1280</dim>
+					<dim>3000</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="73">
+					<dim>-1</dim>
+					<dim>1280</dim>
+					<dim>3000</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="13" name="self.conv2.weight" type="Const" version="opset1">
+			<data element_type="u8" shape="1280, 1280, 3" offset="316160" size="4915200" />
+			<output>
+				<port id="0" precision="U8">
+					<dim>1280</dim>
+					<dim>1280</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="14" name="Convert_36987" type="Convert" version="opset1">
+			<data destination_type="f16" />
+			<input>
+				<port id="0" precision="U8">
+					<dim>1280</dim>
+					<dim>1280</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP16">
+					<dim>1280</dim>
+					<dim>1280</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="15" name="self.conv2.weight/zero_point" type="Const" version="opset1">
+			<data element_type="u8" shape="1280, 1, 1" offset="5231360" size="1280" />
+			<output>
+				<port id="0" precision="U8">
+					<dim>1280</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="16" name="Convert_36990" type="Convert" version="opset1">
+			<data destination_type="f16" />
+			<input>
+				<port id="0" precision="U8">
+					<dim>1280</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP16">
+					<dim>1280</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="17" name="self.conv2.weight/zero_point/subtract" type="Subtract" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1280</dim>
+					<dim>1280</dim>
+					<dim>3</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1280</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1280</dim>
+					<dim>1280</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="18" name="self.conv2.weight/scale" type="Const" version="opset1">
+			<data element_type="f16" shape="1280, 1, 1" offset="5232640" size="2560" />
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1280</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="19" name="self.conv2.weight/fq_weights_1" type="Multiply" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1280</dim>
+					<dim>1280</dim>
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+				<port id="1" precision="FP16">
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+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1280</dim>
+					<dim>1280</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="20" name="self.conv2.weight/fq_weights_1/convert" type="Convert" version="opset1">
+			<data destination_type="f32" />
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+				<port id="0" precision="FP16">
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+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="21" name="__module.conv2/aten::_convolution/Convolution" type="Convolution" version="opset1">
+			<data strides="2" dilations="1" pads_begin="1" pads_end="1" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
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+			</input>
+			<output>
+				<port id="2" precision="FP32">
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+			</output>
+		</layer>
+		<layer id="22" name="__module.conv2/aten::_convolution/Reshape" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 1280, 1" offset="5235200" size="5120" />
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+		</layer>
+		<layer id="23" name="__module.conv2/aten::_convolution/Add" type="Add" version="opset1">
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+		</layer>
+		<layer id="24" name="aten::gelu/Gelu_1" type="Gelu" version="opset7">
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+		<layer id="25" name="Constant_172" type="Const" version="opset1">
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+		<layer id="30" name="__module.layers.0.self_attn_layer_norm/aten::layer_norm/MVN" type="MVN" version="opset6">
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+		<Runtime_version value="2024.5.0-16901-32aaa2fbd96" />
+		<conversion_parameters>
+			<framework value="pytorch" />
+			<is_python_object value="True" />
+		</conversion_parameters>
+		<nncf>
+			<friendly_names_were_updated value="True" />
+			<weight_compression>
+				<advanced_parameters value="{'awq_params': {'subset_size': 32, 'percent_to_apply': 0.002, 'alpha_min': 0.0, 'alpha_max': 1.0, 'steps': 100}, 'scale_estimation_params': {'subset_size': 64, 'initial_steps': 5, 'scale_steps': 5, 'weight_penalty': -1.0}, 'gptq_params': {'damp_percent': 0.1, 'block_size': 128, 'subset_size': 128}, 'lora_correction_params': {'adapter_rank': 8, 'num_iterations': 3, 'apply_regularization': True, 'subset_size': 128, 'use_int8_adapters': True}}" />
+				<all_layers value="False" />
+				<awq value="False" />
+				<gptq value="False" />
+				<group_size value="128" />
+				<ignored_scope value="[]" />
+				<lora_correction value="False" />
+				<mode value="int4_asym" />
+				<ratio value="1.0" />
+				<scale_estimation value="False" />
+				<sensitivity_metric value="weight_quantization_error" />
+			</weight_compression>
+		</nncf>
+		<optimum>
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+			<optimum_version value="1.23.1" />
+			<pytorch_version value="2.5.0" />
+			<transformers_version value="4.45.2" />
+		</optimum>
+	</rt_info>
+</net>