diff --git "a/multilang/ggml-tiny-encoder-openvino-int4/ggml-tiny-encoder-openvino.xml" "b/multilang/ggml-tiny-encoder-openvino-int4/ggml-tiny-encoder-openvino.xml"
new file mode 100644--- /dev/null
+++ "b/multilang/ggml-tiny-encoder-openvino-int4/ggml-tiny-encoder-openvino.xml"
@@ -0,0 +1,10072 @@
+<?xml version="1.0"?>
+<net name="main_graph" version="11">
+	<layers>
+		<layer id="0" name="mel" type="Parameter" version="opset1">
+			<data shape="1,80,2000" element_type="f32" />
+			<output>
+				<port id="0" precision="FP32" names="mel">
+					<dim>1</dim>
+					<dim>80</dim>
+					<dim>2000</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="1" name="onnx::Conv_518" type="Const" version="opset1">
+			<data element_type="u8" shape="384, 80, 3" offset="0" size="92160" />
+			<output>
+				<port id="0" precision="U8">
+					<dim>384</dim>
+					<dim>80</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="2" name="Convert_457" type="Convert" version="opset1">
+			<data destination_type="f32" />
+			<input>
+				<port id="0" precision="U8">
+					<dim>384</dim>
+					<dim>80</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32">
+					<dim>384</dim>
+					<dim>80</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="3" name="onnx::Conv_518/zero_point" type="Const" version="opset1">
+			<data element_type="u8" shape="384, 1, 1" offset="92160" size="384" />
+			<output>
+				<port id="0" precision="U8">
+					<dim>384</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="4" name="Convert_460" type="Convert" version="opset1">
+			<data destination_type="f32" />
+			<input>
+				<port id="0" precision="U8">
+					<dim>384</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32">
+					<dim>384</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="5" name="Subtract_461" type="Subtract" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>384</dim>
+					<dim>80</dim>
+					<dim>3</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>384</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>384</dim>
+					<dim>80</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="6" name="onnx::Conv_518/scale" type="Const" version="opset1">
+			<data element_type="f16" shape="384, 1, 1" offset="92544" size="768" />
+			<output>
+				<port id="0" precision="FP16">
+					<dim>384</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="7" name="onnx::Conv_518/scale_convert" type="Convert" version="opset1">
+			<data destination_type="f32" />
+			<input>
+				<port id="0" precision="FP16">
+					<dim>384</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32">
+					<dim>384</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="8" name="onnx::Conv_518/fq_weights_1" type="Multiply" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>384</dim>
+					<dim>80</dim>
+					<dim>3</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>384</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>384</dim>
+					<dim>80</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="9" name="/conv1/Conv/WithoutBiases" 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>2000</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>384</dim>
+					<dim>80</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>384</dim>
+					<dim>2000</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="10" name="onnx::Conv_519" type="Const" version="opset1">
+			<data element_type="f32" shape="384" offset="93312" size="1536" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_519">
+					<dim>384</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="11" name="Constant_72" type="Const" version="opset1">
+			<data element_type="i64" shape="1" offset="94848" size="8" />
+			<output>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="12" name="ShapeOf_79" type="ShapeOf" version="opset3">
+			<data output_type="i64" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>384</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="13" name="ShapeOf_70" type="ShapeOf" version="opset3">
+			<data output_type="i64" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>384</dim>
+					<dim>2000</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64">
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="14" name="ShapeOf_71" type="ShapeOf" version="opset3">
+			<data output_type="i64" />
+			<input>
+				<port id="0" precision="I64">
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="15" name="Constant_73" type="Const" version="opset1">
+			<data element_type="i64" shape="1" offset="94856" size="8" />
+			<output>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="16" name="Subtract_74" type="Subtract" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="17" name="Broadcast_75" type="Broadcast" version="opset3">
+			<data mode="numpy" />
+			<input>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="18" name="Concat_80" type="Concat" version="opset1">
+			<data axis="0" />
+			<input>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+				<port id="2" precision="I64">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="3" precision="I64">
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="19" name="Reshape_81" type="Reshape" version="opset1">
+			<data special_zero="false" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>384</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>384</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="20" name="/conv1/Conv" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>384</dim>
+					<dim>2000</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>384</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="/conv1/Conv_output_0">
+					<dim>1</dim>
+					<dim>384</dim>
+					<dim>2000</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="21" name="/Constant" type="Const" version="opset1">
+			<data element_type="f32" shape="" offset="94864" size="4" />
+			<output>
+				<port id="0" precision="FP32" names="/Constant_output_0" />
+			</output>
+		</layer>
+		<layer id="22" name="/Div" type="Divide" version="opset1">
+			<data auto_broadcast="numpy" m_pythondiv="true" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>384</dim>
+					<dim>2000</dim>
+				</port>
+				<port id="1" precision="FP32" />
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="/Div_output_0">
+					<dim>1</dim>
+					<dim>384</dim>
+					<dim>2000</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="23" name="/Erf" type="Erf" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>384</dim>
+					<dim>2000</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="/Erf_output_0">
+					<dim>1</dim>
+					<dim>384</dim>
+					<dim>2000</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="24" name="/Constant_1" type="Const" version="opset1">
+			<data element_type="f32" shape="" offset="94868" size="4" />
+			<output>
+				<port id="0" precision="FP32" names="/Constant_1_output_0" />
+			</output>
+		</layer>
+		<layer id="25" name="/Add" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>384</dim>
+					<dim>2000</dim>
+				</port>
+				<port id="1" precision="FP32" />
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="/Add_output_0">
+					<dim>1</dim>
+					<dim>384</dim>
+					<dim>2000</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="26" name="/Mul" type="Multiply" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>384</dim>
+					<dim>2000</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>384</dim>
+					<dim>2000</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="/Mul_output_0">
+					<dim>1</dim>
+					<dim>384</dim>
+					<dim>2000</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="27" name="/Constant_2" type="Const" version="opset1">
+			<data element_type="f32" shape="" offset="94872" size="4" />
+			<output>
+				<port id="0" precision="FP32" names="/Constant_2_output_0" />
+			</output>
+		</layer>
+		<layer id="28" name="/Mul_1" type="Multiply" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>384</dim>
+					<dim>2000</dim>
+				</port>
+				<port id="1" precision="FP32" />
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="/Mul_1_output_0">
+					<dim>1</dim>
+					<dim>384</dim>
+					<dim>2000</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="29" name="onnx::Conv_520" type="Const" version="opset1">
+			<data element_type="u8" shape="384, 384, 3" offset="94876" size="442368" />
+			<output>
+				<port id="0" precision="U8">
+					<dim>384</dim>
+					<dim>384</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="30" name="Convert_468" type="Convert" version="opset1">
+			<data destination_type="f32" />
+			<input>
+				<port id="0" precision="U8">
+					<dim>384</dim>
+					<dim>384</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32">
+					<dim>384</dim>
+					<dim>384</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="31" name="onnx::Conv_520/zero_point" type="Const" version="opset1">
+			<data element_type="u8" shape="384, 1, 1" offset="537244" size="384" />
+			<output>
+				<port id="0" precision="U8">
+					<dim>384</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="32" name="Convert_471" type="Convert" version="opset1">
+			<data destination_type="f32" />
+			<input>
+				<port id="0" precision="U8">
+					<dim>384</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32">
+					<dim>384</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="33" name="Subtract_472" type="Subtract" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>384</dim>
+					<dim>384</dim>
+					<dim>3</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>384</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>384</dim>
+					<dim>384</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="34" name="onnx::Conv_520/scale" type="Const" version="opset1">
+			<data element_type="f16" shape="384, 1, 1" offset="537628" size="768" />
+			<output>
+				<port id="0" precision="FP16">
+					<dim>384</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="35" name="onnx::Conv_520/scale_convert" type="Convert" version="opset1">
+			<data destination_type="f32" />
+			<input>
+				<port id="0" precision="FP16">
+					<dim>384</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32">
+					<dim>384</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="36" name="onnx::Conv_520/fq_weights_1" type="Multiply" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>384</dim>
+					<dim>384</dim>
+					<dim>3</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>384</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>384</dim>
+					<dim>384</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="37" name="/conv2/Conv/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="2" dilations="1" pads_begin="1" pads_end="1" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>384</dim>
+					<dim>2000</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>384</dim>
+					<dim>384</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>384</dim>
+					<dim>1000</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="38" name="onnx::Conv_521" type="Const" version="opset1">
+			<data element_type="f32" shape="384" offset="538396" size="1536" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_521">
+					<dim>384</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="39" name="Constant_96" type="Const" version="opset1">
+			<data element_type="i64" shape="1" offset="94848" size="8" />
+			<output>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="40" name="ShapeOf_103" type="ShapeOf" version="opset3">
+			<data output_type="i64" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>384</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="41" name="ShapeOf_94" type="ShapeOf" version="opset3">
+			<data output_type="i64" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>384</dim>
+					<dim>1000</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64">
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="42" name="ShapeOf_95" type="ShapeOf" version="opset3">
+			<data output_type="i64" />
+			<input>
+				<port id="0" precision="I64">
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
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+		<layer id="85" name="/blocks.0/attn/query/MatMul" type="MatMul" version="opset1">
+			<data transpose_a="false" transpose_b="false" />
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+				<port id="0" precision="FP32">
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+					<dim>1</dim>
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+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
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+			</output>
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+			</output>
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+				<port id="0" precision="U4">
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+			</output>
+		</layer>
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+			</output>
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+				<port id="0" precision="U4">
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+			<data destination_type="f32" />
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+			<data auto_broadcast="numpy" />
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