diff --git a/backends/mediatek/partitioner.py b/backends/mediatek/partitioner.py
index b9e342e937d..f15c058507a 100644
--- a/backends/mediatek/partitioner.py
+++ b/backends/mediatek/partitioner.py
@@ -81,6 +81,7 @@ def ops_to_not_decompose(
             torch.ops.aten.upsample_bilinear2d.vec,
             torch.ops.aten.upsample_nearest2d.default,
             torch.ops.aten.upsample_nearest2d.vec,
+            torch.ops.aten._safe_softmax.default,
         ]
         return (ops_not_decompose, None)
 
diff --git a/backends/mediatek/scripts/mtk_build.sh b/backends/mediatek/scripts/mtk_build.sh
index 6c935b3c80c..97b766807ff 100755
--- a/backends/mediatek/scripts/mtk_build.sh
+++ b/backends/mediatek/scripts/mtk_build.sh
@@ -33,6 +33,7 @@ rm -rf cmake-android-out && mkdir cmake-android-out && cd cmake-android-out
 cmake -DBUCK2="$BUCK_PATH" \
       -DCMAKE_TOOLCHAIN_FILE="$ANDROID_NDK/build/cmake/android.toolchain.cmake" \
       -DANDROID_ABI=arm64-v8a \
+      -DANDROID_PLATFORM=android-26 \
       -DEXECUTORCH_BUILD_NEURON=ON \
       -DNEURON_BUFFER_ALLOCATOR_LIB="$NEURON_BUFFER_ALLOCATOR_LIB" \
       ..
diff --git a/examples/mediatek/aot_utils/oss_utils/utils.py b/examples/mediatek/aot_utils/oss_utils/utils.py
index 25362788e31..f788735fa95 100755
--- a/examples/mediatek/aot_utils/oss_utils/utils.py
+++ b/examples/mediatek/aot_utils/oss_utils/utils.py
@@ -5,7 +5,7 @@
 # LICENSE file in the root directory of this source tree.
 
 import os
-from typing import Optional
+from typing import Optional, Dict
 
 import torch
 from executorch import exir
@@ -23,6 +23,8 @@ def build_executorch_binary(
     file_name,
     dataset,
     quant_dtype: Optional[Precision] = None,
+    skip_op_name: Optional[set] = None,
+    skip_op_type: Optional[set] = None
 ):
     if quant_dtype is not None:
         quantizer = NeuropilotQuantizer()
@@ -46,14 +48,12 @@ def build_executorch_binary(
     from executorch.exir.program._program import to_edge_transform_and_lower
 
     edge_compile_config = exir.EdgeCompileConfig(_check_ir_validity=False)
-    # skipped op names are used for deeplabV3 model
     neuro_partitioner = NeuropilotPartitioner(
         [],
-        op_names_to_skip={
-            "aten_convolution_default_106",
-            "aten_convolution_default_107",
-        },
+        op_types_to_skip=skip_op_type,
+        op_names_to_skip=skip_op_name,
     )
+
     edge_prog = to_edge_transform_and_lower(
         aten_dialect,
         compile_config=edge_compile_config,
diff --git a/examples/mediatek/model_export_scripts/dcgan.py b/examples/mediatek/model_export_scripts/dcgan.py
new file mode 100755
index 00000000000..411cbd4abae
--- /dev/null
+++ b/examples/mediatek/model_export_scripts/dcgan.py
@@ -0,0 +1,102 @@
+# Copyright (c) MediaTek Inc.
+# All rights reserved
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+import os
+import sys
+
+if os.getcwd() not in sys.path:
+    sys.path.append(os.getcwd())
+import json
+import os
+import numpy as np
+import argparse
+
+import torch
+import dcgan_main
+from executorch.backends.mediatek import Precision
+from aot_utils.oss_utils.utils import (
+    build_executorch_binary,
+    make_output_dir,
+)
+
+
+class NhwcWrappedModel(torch.nn.Module):
+    def __init__(self, is_gen=True):
+        super(NhwcWrappedModel, self).__init__()
+        if is_gen:
+            self.dcgan = dcgan_main.Generator()
+        else:
+            self.dcgan = dcgan_main.Discriminator()
+
+    def forward(self, input1):
+        nchw_input1 = input1.permute(0, 3, 1, 2)
+        output = self.dcgan(nchw_input1)
+        return output
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "-a",
+        "--artifact",
+        help="path for storing generated artifacts by this example. "
+        "Default ./dcgan",
+        default="./dcgan",
+        type=str,
+    )
+
+    args = parser.parse_args()
+
+    # ensure the working directory exist.
+    os.makedirs(args.artifact, exist_ok=True)
+
+    # prepare dummy data
+    inputG = torch.randn(1, 1, 1, 100)
+    inputD = torch.randn(1, 64, 64, 3)
+
+    # build Generator
+    netG_instance = NhwcWrappedModel(True)
+    netG_pte_filename = "dcgan_netG_mtk"
+    build_executorch_binary(
+        netG_instance.eval(),
+        (torch.randn(1, 1, 1, 100),),
+        f"{args.artifact}/{netG_pte_filename}",
+        [(inputG,)],
+        quant_dtype=Precision.A8W8,
+    )
+
+    # build Discriminator
+    netD_instance = NhwcWrappedModel(False)
+    netD_pte_filename = "dcgan_netD_mtk"
+    build_executorch_binary(
+        netD_instance.eval(),
+        (torch.randn(1, 64, 64, 3),),
+        f"{args.artifact}/{netD_pte_filename}",
+        [(inputD,)],
+        quant_dtype=Precision.A8W8,
+    )
+
+    # save data to inference on device
+    input_list_file = f"{args.artifact}/input_list_G.txt"
+    with open(input_list_file, "w") as f:
+        f.write("inputG_0_0.bin")
+        f.flush()
+    file_name = f"{args.artifact}/inputG_0_0.bin"
+    inputG.detach().numpy().tofile(file_name)
+    file_name = f"{args.artifact}/goldenG_0_0.bin"
+    goldenG = netG_instance(inputG)
+    goldenG.detach().numpy().tofile(file_name)
+
+    input_list_file = f"{args.artifact}/input_list_D.txt"
+    with open(input_list_file, "w") as f:
+        f.write("inputD_0_0.bin")
+        f.flush()
+    file_name = f"{args.artifact}/inputD_0_0.bin"
+    inputD.detach().numpy().tofile(file_name)
+    file_name = f"{args.artifact}/goldenD_0_0.bin"
+    goldenD = netD_instance(inputD)
+    goldenD.detach().numpy().tofile(file_name)
+
diff --git a/examples/mediatek/model_export_scripts/dcgan_main.py b/examples/mediatek/model_export_scripts/dcgan_main.py
new file mode 100755
index 00000000000..77976c5c63b
--- /dev/null
+++ b/examples/mediatek/model_export_scripts/dcgan_main.py
@@ -0,0 +1,70 @@
+"""Ref https://github.com/pytorch/examples/blob/main/dcgan/main.py"""
+
+import torch.nn as nn
+
+
+class Generator(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.main = nn.Sequential(
+            # input is Z, going into a convolution
+            nn.ConvTranspose2d(100, 64 * 8, 4, 1, 0, bias=False),
+            nn.BatchNorm2d(64 * 8),
+            nn.ReLU(True),
+            # state size. (64*8) x 4 x 4
+            nn.ConvTranspose2d(64 * 8, 64 * 4, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(64 * 4),
+            nn.ReLU(True),
+            # state size. (64*4) x 8 x 8
+            nn.ConvTranspose2d(64 * 4, 64 * 2, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(64 * 2),
+            nn.ReLU(True),
+            # state size. (64*2) x 16 x 16
+            nn.ConvTranspose2d(64 * 2, 64, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(64),
+            nn.ReLU(True),
+            # state size. (64) x 32 x 32
+            nn.ConvTranspose2d(64, 3, 4, 2, 1, bias=False),
+            nn.Tanh()
+            # state size. (3) x 64 x 64
+        )
+
+    def forward(self, input):
+        output = self.main(input)
+        return output
+
+# main_netG_input_shape = [1, 100, 1, 1]
+# model = Generator()
+
+
+class Discriminator(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.main = nn.Sequential(
+            # input is (3) x 64 x 64
+            nn.Conv2d(3, 64, 4, 2, 1, bias=False),
+            nn.LeakyReLU(0.2, inplace=True),
+            # state size. (64) x 32 x 32
+            nn.Conv2d(64, 64 * 2, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(64 * 2),
+            nn.LeakyReLU(0.2, inplace=True),
+            # state size. (64*2) x 16 x 16
+            nn.Conv2d(64 * 2, 64 * 4, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(64 * 4),
+            nn.LeakyReLU(0.2, inplace=True),
+            # state size. (64*4) x 8 x 8
+            nn.Conv2d(64 * 4, 64 * 8, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(64 * 8),
+            nn.LeakyReLU(0.2, inplace=True),
+            # state size. (64*8) x 4 x 4
+            nn.Conv2d(64 * 8, 1, 4, 1, 0, bias=False),
+            nn.Sigmoid()
+        )
+
+    def forward(self, input):
+        output = self.main(input)
+
+        return output.view(-1, 1).squeeze(1)
+
+# main_netD_input_shape = [1, 3, 64, 64]
+# model = Discriminator()
diff --git a/examples/mediatek/model_export_scripts/deeplab_v3.py b/examples/mediatek/model_export_scripts/deeplab_v3.py
index da6766c0f54..f8113f006ef 100755
--- a/examples/mediatek/model_export_scripts/deeplab_v3.py
+++ b/examples/mediatek/model_export_scripts/deeplab_v3.py
@@ -3,16 +3,19 @@
 #
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
+import os
+import sys
 
+if os.getcwd() not in sys.path:
+    sys.path.append(os.getcwd())
 import argparse
-import os
 import random
 
 import numpy as np
 
 import torch
 from executorch.backends.mediatek import Precision
-from executorch.examples.mediatek.aot_utils.oss_utils.utils import (
+from aot_utils.oss_utils.utils import (
     build_executorch_binary,
 )
 from executorch.examples.models.deeplab_v3 import DeepLabV3ResNet101Model
@@ -26,7 +29,7 @@ def __init__(self):
     def forward(self, input1):
         nchw_input1 = input1.permute(0, 3, 1, 2)
         nchw_output = self.deeplabv3(nchw_input1)
-        return nchw_output.permute(0, 2, 3, 1)
+        return nchw_output
 
 
 def get_dataset(data_size, dataset_dir, download):
@@ -121,4 +124,8 @@ def get_dataset(data_size, dataset_dir, download):
         f"{args.artifact}/{pte_filename}",
         inputs,
         quant_dtype=Precision.A8W8,
+        skip_op_name = {
+            "aten_convolution_default_106",
+            "aten_convolution_default_107",
+        },
     )
diff --git a/examples/mediatek/model_export_scripts/edsr.py b/examples/mediatek/model_export_scripts/edsr.py
index 4192d67e569..6ed00364845 100755
--- a/examples/mediatek/model_export_scripts/edsr.py
+++ b/examples/mediatek/model_export_scripts/edsr.py
@@ -6,12 +6,15 @@
 
 import argparse
 import os
+import sys
 
+if os.getcwd() not in sys.path:
+    sys.path.append(os.getcwd())
 import numpy as np
 
 import torch
 from executorch.backends.mediatek import Precision
-from executorch.examples.mediatek.aot_utils.oss_utils.utils import (
+from aot_utils.oss_utils.utils import (
     build_executorch_binary,
 )
 from executorch.examples.models.edsr import EdsrModel
diff --git a/examples/mediatek/model_export_scripts/emformer_rnnt.py b/examples/mediatek/model_export_scripts/emformer_rnnt.py
new file mode 100755
index 00000000000..aae34a257a3
--- /dev/null
+++ b/examples/mediatek/model_export_scripts/emformer_rnnt.py
@@ -0,0 +1,162 @@
+# Copyright (c) MediaTek Inc.
+# All rights reserved
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+import os
+import sys
+
+if os.getcwd() not in sys.path:
+    sys.path.append(os.getcwd())
+import json
+import os
+import numpy as np
+import argparse
+
+import torch
+from executorch.examples.models.emformer_rnnt import (
+    EmformerRnntTranscriberModel,
+    EmformerRnntPredictorModel,
+    EmformerRnntJoinerModel,
+)
+from executorch.backends.mediatek import Precision
+from aot_utils.oss_utils.utils import build_executorch_binary
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "-a",
+        "--artifact",
+        help="path for storing generated artifacts by this example. "
+        "Default ./emformer_rnnt",
+        default="./emformer_rnnt",
+        type=str,
+    )
+
+    args = parser.parse_args()
+
+    # ensure the working directory exist.
+    os.makedirs(args.artifact, exist_ok=True)
+
+    # build Transcriber
+    print("Build Transcriber")
+    transcriber = EmformerRnntTranscriberModel()
+    t_model = transcriber.get_eager_model()
+    inputs = transcriber.get_example_inputs()
+    pte_filename = 'emformer_rnnt_t_mtk'
+    build_executorch_binary(
+        t_model.eval(),
+       inputs,
+        f"{args.artifact}/{pte_filename}",
+        [inputs],
+        quant_dtype=Precision.A8W8,
+        skip_op_type={
+            "aten.where.self",
+        },
+        skip_op_name={
+            "aten_div_tensor_mode",
+            "aten_unsqueeze_copy_default",
+            "aten_unsqueeze_copy_default_1",
+            "aten_unsqueeze_copy_default_2",
+            "aten_unsqueeze_copy_default_3",
+            "aten_unsqueeze_copy_default_4",
+            "aten_unsqueeze_copy_default_5",
+            "aten_unsqueeze_copy_default_6",
+            "aten_unsqueeze_copy_default_7",
+            "aten_unsqueeze_copy_default_8",
+            "aten_unsqueeze_copy_default_9",
+            "aten_unsqueeze_copy_default_10",
+            "aten_unsqueeze_copy_default_11",
+            "aten_unsqueeze_copy_default_12",
+            "aten_unsqueeze_copy_default_13",
+            "aten_unsqueeze_copy_default_14",
+            "aten_unsqueeze_copy_default_15",
+            "aten_unsqueeze_copy_default_16",
+            "aten_unsqueeze_copy_default_17",
+            "aten_unsqueeze_copy_default_18",
+            "aten_unsqueeze_copy_default_19",
+        },
+    )
+    
+    # save data to inference on device
+    input_list_file = f"{args.artifact}/input_list_t.txt"
+    with open(input_list_file, "w") as f:
+        f.write("input_t_0_0.bin input_t_0_1.bin")
+        f.flush()
+    for idx, data in enumerate(inputs[0]):
+        file_name = f"{args.artifact}/input_t_0_{idx}.bin"
+        data.detach().numpy().tofile(file_name)
+    golden = t_model(inputs[0])
+    for idx, data in enumerate(golden):
+        file_name = f"{args.artifact}/golden_t_0_{idx}.bin"
+        data.detach().numpy().tofile(file_name)
+
+    # build Predictor
+    print("Build Predictor")
+    predictor = EmformerRnntPredictorModel()
+    p_model = predictor.get_eager_model()
+    inputs = predictor.get_example_inputs()
+    pte_filename = 'emformer_rnnt_p_mtk'
+    build_executorch_binary(
+        p_model.eval(),
+        inputs,
+        f"{args.artifact}/{pte_filename}",
+        [inputs],
+        quant_dtype=Precision.A8W8,
+        skip_op_name={
+            "aten_permute_copy_default",
+            "aten_embedding_default",
+        },
+    )
+
+    # save data to inference on device
+    input_list_file = f"{args.artifact}/input_list_p.txt"
+    with open(input_list_file, "w") as f:
+        f.write("input_p_0_0.bin input_p_0_1.bin input_p_0_2.bin")
+        f.flush()
+    for idx, data in enumerate(inputs[0]):
+        file_name = f"{args.artifact}/input_p_0_{idx}.bin"
+        try:
+            data.detach().numpy().tofile(file_name)
+        except:
+            pass
+    golden = p_model(inputs[0])
+    for idx, data in enumerate(golden):
+        file_name = f"{args.artifact}/golden_p_0_{idx}.bin"
+        try:
+            data.detach().numpy().tofile(file_name)
+        except:
+            pass
+
+    # build Joiner
+    print("Build Joiner")
+    joiner = EmformerRnntJoinerModel()
+    j_model = joiner.get_eager_model()
+    inputs = joiner.get_example_inputs()
+    pte_filename = 'emformer_rnnt_j_mtk'
+    build_executorch_binary(
+        j_model.eval(),
+        inputs,
+        f"{args.artifact}/{pte_filename}",
+        [inputs],
+        quant_dtype=Precision.A8W8,
+        skip_op_name={
+            "aten_add_tensor",
+        },
+    )
+
+    # save data to inference on device
+    input_list_file = f"{args.artifact}/input_list_j.txt"
+    with open(input_list_file, "w") as f:
+        f.write("input_j_0_0.bin input_j_0_1.bin input_j_0_2.bin input_j_0_3.bin")
+        f.flush()
+    for idx, data in enumerate(inputs[0]):
+        file_name = f"{args.artifact}/input_j_0_{idx}.bin"
+        data.detach().numpy().tofile(file_name)
+    golden = j_model(inputs[0])
+    for idx, data in enumerate(golden):
+        file_name = f"{args.artifact}/golden_j_0_{idx}.bin"
+        data.detach().numpy().tofile(file_name)
+
diff --git a/examples/mediatek/model_export_scripts/inception_v3.py b/examples/mediatek/model_export_scripts/inception_v3.py
index c28bd85b402..5bc8e99e09e 100755
--- a/examples/mediatek/model_export_scripts/inception_v3.py
+++ b/examples/mediatek/model_export_scripts/inception_v3.py
@@ -6,10 +6,14 @@
 
 import argparse
 import os
+import sys
+
+if os.getcwd() not in sys.path:
+    sys.path.append(os.getcwd())
 
 import torch
 from executorch.backends.mediatek import Precision
-from executorch.examples.mediatek.aot_utils.oss_utils.utils import (
+from aot_utils.oss_utils.utils import (
     build_executorch_binary,
 )
 from executorch.examples.models.inception_v3 import InceptionV3Model
diff --git a/examples/mediatek/model_export_scripts/inception_v4.py b/examples/mediatek/model_export_scripts/inception_v4.py
index ccb2ce16f22..a1ded356e50 100755
--- a/examples/mediatek/model_export_scripts/inception_v4.py
+++ b/examples/mediatek/model_export_scripts/inception_v4.py
@@ -6,10 +6,14 @@
 
 import argparse
 import os
+import sys
+
+if os.getcwd() not in sys.path:
+    sys.path.append(os.getcwd())
 
 import torch
 from executorch.backends.mediatek import Precision
-from executorch.examples.mediatek.aot_utils.oss_utils.utils import (
+from aot_utils.oss_utils.utils import (
     build_executorch_binary,
 )
 from executorch.examples.models.inception_v4 import InceptionV4Model
diff --git a/examples/mediatek/model_export_scripts/mobilebert.py b/examples/mediatek/model_export_scripts/mobilebert.py
new file mode 100755
index 00000000000..c7122a0484c
--- /dev/null
+++ b/examples/mediatek/model_export_scripts/mobilebert.py
@@ -0,0 +1,69 @@
+# Copyright (c) MediaTek Inc.
+# All rights reserved
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+import os
+import sys
+
+if os.getcwd() not in sys.path:
+    sys.path.append(os.getcwd())
+import json
+import os
+import numpy as np
+import argparse
+
+import torch
+from executorch.backends.mediatek import Precision
+from executorch.examples.models.mobilebert import MobileBertModelExample
+from aot_utils.oss_utils.utils import (
+    build_executorch_binary,
+    make_output_dir,
+)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "-a",
+        "--artifact",
+        help="path for storing generated artifacts by this example. "
+        "Default ./mobilebert",
+        default="./mobilebert",
+        type=str,
+    )
+
+    args = parser.parse_args()
+
+    # ensure the working directory exist.
+    os.makedirs(args.artifact, exist_ok=True)
+
+    # build pte
+    pte_filename = "mobilebert_mtk"
+    model = MobileBertModelExample()
+    instance = model.get_eager_model().eval()
+    inputs = model.get_example_inputs()
+    build_executorch_binary(
+        instance.eval(),
+        inputs,
+        f"{args.artifact}/{pte_filename}",
+        [inputs],
+        quant_dtype=Precision.A8W8,
+        skip_op_name={
+            "aten_embedding_default",
+        },
+    )
+
+    #save data to inference on device
+    golden = instance(inputs[0])
+    input_list_file = f"{args.artifact}/input_list.txt"
+    with open(input_list_file, "w") as f:
+        f.write("input_0_0.bin")
+        f.flush()
+    file_name = f"{args.artifact}/input_0_0.bin"
+    inputs[0].detach().numpy().tofile(file_name)
+    file_name = f"{args.artifact}/golden_0_0.bin"
+    for idx, data in enumerate(golden):
+        file_name = f"{args.artifact}/golden_0_{idx}.bin"
+        data.detach().numpy().tofile(file_name)
diff --git a/examples/mediatek/model_export_scripts/mobilenet_v2.py b/examples/mediatek/model_export_scripts/mobilenet_v2.py
index 97f2ed884eb..ff754814a45 100755
--- a/examples/mediatek/model_export_scripts/mobilenet_v2.py
+++ b/examples/mediatek/model_export_scripts/mobilenet_v2.py
@@ -6,10 +6,14 @@
 
 import argparse
 import os
+import sys
+
+if os.getcwd() not in sys.path:
+    sys.path.append(os.getcwd())
 
 import torch
 from executorch.backends.mediatek import Precision
-from executorch.examples.mediatek.aot_utils.oss_utils.utils import (
+from aot_utils.oss_utils.utils import (
     build_executorch_binary,
 )
 from executorch.examples.models.mobilenet_v2 import MV2Model
diff --git a/examples/mediatek/model_export_scripts/mobilenet_v3.py b/examples/mediatek/model_export_scripts/mobilenet_v3.py
index fed2497ca26..aeb75e7526d 100755
--- a/examples/mediatek/model_export_scripts/mobilenet_v3.py
+++ b/examples/mediatek/model_export_scripts/mobilenet_v3.py
@@ -6,10 +6,14 @@
 
 import argparse
 import os
+import sys
+
+if os.getcwd() not in sys.path:
+    sys.path.append(os.getcwd())
 
 import torch
 from executorch.backends.mediatek import Precision
-from executorch.examples.mediatek.aot_utils.oss_utils.utils import (
+from aot_utils.oss_utils.utils import (
     build_executorch_binary,
 )
 from executorch.examples.models.mobilenet_v3 import MV3Model
diff --git a/examples/mediatek/model_export_scripts/resnet18.py b/examples/mediatek/model_export_scripts/resnet18.py
index 2f3af57e7f3..ba2d103efef 100755
--- a/examples/mediatek/model_export_scripts/resnet18.py
+++ b/examples/mediatek/model_export_scripts/resnet18.py
@@ -6,10 +6,14 @@
 
 import argparse
 import os
+import sys
+
+if os.getcwd() not in sys.path:
+    sys.path.append(os.getcwd())
 
 import torch
 from executorch.backends.mediatek import Precision
-from executorch.examples.mediatek.aot_utils.oss_utils.utils import (
+from aot_utils.oss_utils.utils import (
     build_executorch_binary,
 )
 from executorch.examples.models.resnet import ResNet18Model
diff --git a/examples/mediatek/model_export_scripts/resnet50.py b/examples/mediatek/model_export_scripts/resnet50.py
index ce23842447b..ad43cb5d943 100755
--- a/examples/mediatek/model_export_scripts/resnet50.py
+++ b/examples/mediatek/model_export_scripts/resnet50.py
@@ -6,10 +6,14 @@
 
 import argparse
 import os
+import sys
+
+if os.getcwd() not in sys.path:
+    sys.path.append(os.getcwd())
 
 import torch
 from executorch.backends.mediatek import Precision
-from executorch.examples.mediatek.aot_utils.oss_utils.utils import (
+from aot_utils.oss_utils.utils import (
     build_executorch_binary,
 )
 from executorch.examples.models.resnet import ResNet50Model
diff --git a/examples/mediatek/model_export_scripts/vit_b_16.py b/examples/mediatek/model_export_scripts/vit_b_16.py
new file mode 100755
index 00000000000..be43a938333
--- /dev/null
+++ b/examples/mediatek/model_export_scripts/vit_b_16.py
@@ -0,0 +1,95 @@
+# Copyright (c) MediaTek Inc.
+# All rights reserved
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+import os
+import sys
+
+if os.getcwd() not in sys.path:
+    sys.path.append(os.getcwd())
+import json
+import numpy as np
+import argparse
+
+import torch
+from executorch.backends.mediatek import Precision
+from executorch.examples.models.torchvision_vit import TorchVisionViTModel
+from aot_utils.oss_utils.utils import (
+    build_executorch_binary,
+    make_output_dir,
+)
+
+
+class NhwcWrappedModel(torch.nn.Module):
+    def __init__(self):
+        super(NhwcWrappedModel, self).__init__()
+        self.vit_b_16 = TorchVisionViTModel().get_eager_model()
+
+    def forward(self, input1):
+        nchw_input1 = input1.permute(0, 3, 1, 2)
+        output = self.vit_b_16(nchw_input1)
+        return output
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "-a",
+        "--artifact",
+        help="path for storing generated artifacts by this example. "
+        "Default ./vit_b_16",
+        default="./vit_b_16",
+        type=str,
+    )
+
+    args = parser.parse_args()
+
+    # ensure the working directory exist.
+    os.makedirs(args.artifact, exist_ok=True)
+
+    # build pte
+    pte_filename = "vit_b_16_mtk"
+    instance = NhwcWrappedModel()
+
+    # if dropout.p = 0, change probability to 1e-6 to prevent -inf when quantize
+    for name, module in instance.named_modules():
+        if type(module) == torch.nn.Dropout:
+            if module.p == 0:
+                module.p = 1e-6
+
+    inputs = (torch.randn(1, 224, 224, 3),)
+    build_executorch_binary(
+        instance.eval(),
+        (torch.randn(1, 224, 224, 3),),
+        f"{args.artifact}/{pte_filename}",
+        [inputs],
+        quant_dtype=Precision.A8W8,
+        skip_op_name = {
+            "aten_permute_copy_default_4",
+            "aten_permute_copy_default_18",
+            "aten_permute_copy_default_32",
+            "aten_permute_copy_default_46",
+            "aten_permute_copy_default_60",
+            "aten_permute_copy_default_74",
+            "aten_permute_copy_default_88",
+            "aten_permute_copy_default_102",
+            "aten_permute_copy_default_116",
+            "aten_permute_copy_default_130",
+            "aten_permute_copy_default_144",
+            "aten_permute_copy_default_158",
+        },
+    )
+
+    # save data to inference on device
+    input_list_file = f"{args.artifact}/input_list.txt"
+    with open(input_list_file, "w") as f:
+        f.write("input_0_0.bin")
+        f.flush()
+    file_name = f"{args.artifact}/input_0_0.bin"
+    inputs[0].detach().numpy().tofile(file_name)
+    file_name = f"{args.artifact}/golden_0_0.bin"
+    golden = instance(inputs[0])
+    golden.detach().numpy().tofile(file_name)
+
diff --git a/examples/mediatek/model_export_scripts/wav2letter.py b/examples/mediatek/model_export_scripts/wav2letter.py
new file mode 100755
index 00000000000..f999ec0f55b
--- /dev/null
+++ b/examples/mediatek/model_export_scripts/wav2letter.py
@@ -0,0 +1,71 @@
+# Copyright (c) MediaTek Inc.
+# All rights reserved
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+import os
+import sys
+
+if os.getcwd() not in sys.path:
+    sys.path.append(os.getcwd())
+import json
+import numpy as np
+import argparse
+
+import torch
+from executorch.backends.mediatek import Precision
+from executorch.examples.models.wav2letter import Wav2LetterModel
+from aot_utils.oss_utils.utils import (
+    build_executorch_binary,
+    make_output_dir,
+)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "-a",
+        "--artifact",
+        help="path for storing generated artifacts by this example. "
+        "Default ./wav2letter",
+        default="./wav2letter",
+        type=str,
+    )
+
+    args = parser.parse_args()
+
+    # ensure the working directory exist.
+    os.makedirs(args.artifact, exist_ok=True)
+
+    # build pte
+    pte_filename = "wav2letter_mtk"
+    model = Wav2LetterModel()
+    instance = model.get_eager_model()
+    inputs = model.get_example_inputs()
+
+    build_executorch_binary(
+        instance.eval(),
+        inputs,
+        f"{args.artifact}/{pte_filename}",
+        [inputs],
+        quant_dtype=Precision.A8W8,
+        skip_op_name = {
+            "aten_convolution_default",
+            "aten_convolution_default_1",
+            "aten_convolution_default_9",
+            "aten__log_softmax_default",
+        },
+    )
+
+    # save data to inference on device
+    input_list_file = f"{args.artifact}/input_list.txt"
+    with open(input_list_file, "w") as f:
+        f.write("input_0_0.bin")
+        f.flush()
+    file_name = f"{args.artifact}/input_0_0.bin"
+    inputs[0].detach().numpy().tofile(file_name)
+    file_name = f"{args.artifact}/golden_0_0.bin"
+    golden = instance(inputs[0])
+    golden.detach().numpy().tofile(file_name)
+
diff --git a/examples/mediatek/mtk_build_examples.sh b/examples/mediatek/mtk_build_examples.sh
index df70489cf2a..87787e373d3 100755
--- a/examples/mediatek/mtk_build_examples.sh
+++ b/examples/mediatek/mtk_build_examples.sh
@@ -39,6 +39,7 @@ main() {
           -DBUCK2="$BUCK_PATH" \
           -DCMAKE_TOOLCHAIN_FILE="$ANDROID_NDK/build/cmake/android.toolchain.cmake" \
           -DANDROID_ABI=arm64-v8a \
+          -DANDROID_PLATFORM=android-26 \
           -DANDROID_NATIVE_API_LEVEL=23 \
           -DEXECUTORCH_BUILD_NEURON=ON \
           -DNEURON_BUFFER_ALLOCATOR_LIB="$NEURON_BUFFER_ALLOCATOR_LIB" \
@@ -58,6 +59,7 @@ main() {
     cmake -DCMAKE_PREFIX_PATH="${cmake_prefix_path}" \
           -DCMAKE_TOOLCHAIN_FILE="$ANDROID_NDK/build/cmake/android.toolchain.cmake" \
           -DANDROID_ABI=arm64-v8a \
+          -DANDROID_PLATFORM=android-26 \
           -DCMAKE_FIND_ROOT_PATH_MODE_PACKAGE=BOTH \
           -DNEURON_BUFFER_ALLOCATOR_LIB="$NEURON_BUFFER_ALLOCATOR_LIB" \
           -B"${example_build_dir}" \
diff --git a/examples/mediatek/shell_scripts/export_oss.sh b/examples/mediatek/shell_scripts/export_oss.sh
index 3da5dc41f94..3e00506c05a 100755
--- a/examples/mediatek/shell_scripts/export_oss.sh
+++ b/examples/mediatek/shell_scripts/export_oss.sh
@@ -26,4 +26,19 @@ then
 elif [ $model = "resnet50" ]
 then
 	python3 model_export_scripts/resnet50.py -d PATH_TO_DATASET
+elif [ $model = "dcgan" ]
+then
+	python3 model_export_scripts/dcgan.py
+elif [ $model = "wav2letter" ]
+then
+	python3 model_export_scripts/wav2letter.py
+elif [ $model = "vit_b_16" ]
+then
+	python3 model_export_scripts/vit_b_16.py
+elif [ $model = "mobilebert" ]
+then
+	python3 model_export_scripts/mobilebert.py
+elif [ $model = "emformer_rnnt" ]
+then
+	python3 model_export_scripts/emformer_rnnt.py
 fi