diff --git a/README.md b/README.md
index 95a607c..c5e20ce 100644
--- a/README.md
+++ b/README.md
@@ -79,7 +79,7 @@ jupyter-notebook --no-browser --allow-root --port=8888
 
 First, create & source a virtual environment:
 ```shell
-conda create -n <virtual-env> python=3.10
+conda create -n <virtual-env> python=3.8
 conda activate <virtual-env>
 ```
 
diff --git a/build/cybersecurity-mlp/build.py b/build/cybersecurity-mlp/build.py
index b5fb380..f97058f 100644
--- a/build/cybersecurity-mlp/build.py
+++ b/build/cybersecurity-mlp/build.py
@@ -31,7 +31,6 @@
 from finn.util.basic import alveo_default_platform
 import os
 import shutil
-from custom_steps import custom_step_mlp_export
 
 # Which platforms to build the networks for
 zynq_platforms = ["Pynq-Z1", "Ultra96", "ZCU104"]
@@ -90,8 +89,7 @@ def platform_to_shell(platform):
         save_intermediate_models=True,
     )
 
-    # Export MLP model to FINN-ONNX
-    model = custom_step_mlp_export(model_name)
+    model = "models/%s.onnx" % model_name
     # Launch FINN compiler to generate bitfile
     build.build_dataflow_cfg(model, cfg)
     # Copy bitfiles into release dir if found
diff --git a/build/cybersecurity-mlp/custom_steps.py b/build/cybersecurity-mlp/custom_steps.py
deleted file mode 100644
index 30ac9bb..0000000
--- a/build/cybersecurity-mlp/custom_steps.py
+++ /dev/null
@@ -1,118 +0,0 @@
-# Copyright (C) 2023, Advanced Micro Devices, Inc.
-# All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions are met:
-#
-# * Redistributions of source code must retain the above copyright notice, this
-#   list of conditions and the following disclaimer.
-#
-# * Redistributions in binary form must reproduce the above copyright notice,
-#   this list of conditions and the following disclaimer in the documentation
-#   and/or other materials provided with the distribution.
-#
-# * Neither the name of FINN nor the names of its
-#   contributors may be used to endorse or promote products derived from
-#   this software without specific prior written permission.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
-# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
-# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
-# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
-# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
-# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
-import numpy as np
-import pkg_resources as pk
-import os
-
-from brevitas.nn import QuantLinear, QuantReLU, QuantIdentity
-import torch
-import torch.nn as nn
-import brevitas.onnx as bo
-from qonnx.core.modelwrapper import ModelWrapper
-from qonnx.core.datatype import DataType
-
-
-# Define export wrapper
-class CybSecMLPForExport(nn.Module):
-    def __init__(self, my_pretrained_model):
-        super(CybSecMLPForExport, self).__init__()
-        self.pretrained = my_pretrained_model
-        self.qnt_output = QuantIdentity(
-            quant_type="binary", scaling_impl_type="const", bit_width=1, min_val=-1.0, max_val=1.0
-        )
-
-    def forward(self, x):
-        # assume x contains bipolar {-1,1} elems
-        # shift from {-1,1} -> {0,1} since that is the
-        # input range for the trained network
-        x = (x + torch.tensor([1.0]).to("cpu")) / 2.0
-        out_original = self.pretrained(x)
-        out_final = self.qnt_output(out_original)  # output as {-1, 1}
-        return out_final
-
-
-def custom_step_mlp_export(model_name):
-    # Define model parameters
-    input_size = 593
-    hidden1 = 64
-    hidden2 = 64
-    hidden3 = 64
-    weight_bit_width = 2
-    act_bit_width = 2
-    num_classes = 1
-
-    # Create model definition from Brevitas library
-    model = nn.Sequential(
-        QuantLinear(input_size, hidden1, bias=True, weight_bit_width=weight_bit_width),
-        nn.BatchNorm1d(hidden1),
-        nn.Dropout(0.5),
-        QuantReLU(act_bit_width=act_bit_width),
-        QuantLinear(hidden1, hidden2, bias=True, weight_bit_width=weight_bit_width),
-        nn.BatchNorm1d(hidden2),
-        nn.Dropout(0.5),
-        QuantReLU(bit_width=act_bit_width),
-        QuantLinear(hidden2, hidden3, bias=True, weight_bit_width=weight_bit_width),
-        nn.BatchNorm1d(hidden3),
-        nn.Dropout(0.5),
-        QuantReLU(bit_width=act_bit_width),
-        QuantLinear(hidden3, num_classes, bias=True, weight_bit_width=weight_bit_width),
-    )
-
-    # Load pre-trained weights
-    assets_dir = pk.resource_filename("finn.qnn-data", "cybsec-mlp/")
-    trained_state_dict = torch.load(assets_dir + "/state_dict.pth")["models_state_dict"][0]
-    model.load_state_dict(trained_state_dict, strict=False)
-
-    # Network surgery: pad input size from 593 to 600 and convert bipolar to binary
-    W_orig = model[0].weight.data.detach().numpy()
-    W_new = np.pad(W_orig, [(0, 0), (0, 7)])
-    model[0].weight.data = torch.from_numpy(W_new)
-
-    model_for_export = CybSecMLPForExport(model)
-
-    # Create directory to save model
-    os.makedirs("models", exist_ok=True)
-    ready_model_filename = "models/%s.onnx" % model_name
-    input_shape = (1, 600)
-
-    # create a QuantTensor instance to mark input as bipolar during export
-    input_a = np.random.randint(0, 1, size=input_shape).astype(np.float32)
-    input_a = 2 * input_a - 1
-    scale = 1.0
-    input_t = torch.from_numpy(input_a * scale)
-
-    # Export to ONNX
-    bo.export_qonnx(model_for_export, export_path=ready_model_filename, input_t=input_t)
-
-    # Set input datatype for FINN's InferDataType transformation to infer the right datatypes
-    model = ModelWrapper(ready_model_filename)
-    model.set_tensor_datatype(model.graph.input[0].name, DataType["BIPOLAR"])
-    model.save(ready_model_filename)
-
-    return ready_model_filename
diff --git a/build/cybersecurity-mlp/models/download-model.sh b/build/cybersecurity-mlp/models/download-model.sh
new file mode 100755
index 0000000..e05c889
--- /dev/null
+++ b/build/cybersecurity-mlp/models/download-model.sh
@@ -0,0 +1,4 @@
+#!/bin/sh
+
+wget https://github.com/Xilinx/finn-examples/releases/download/v0.0.7a/onnx-models-cybersecurity.zip
+unzip -j onnx-models-cybersecurity.zip
diff --git a/build/gtsrb/models/download-model.sh b/build/gtsrb/models/download-model.sh
index 417fb1b..144b82d 100755
--- a/build/gtsrb/models/download-model.sh
+++ b/build/gtsrb/models/download-model.sh
@@ -1,2 +1,3 @@
 #!/bin/bash
-wget https://github.com/fastmachinelearning/qonnx_model_zoo/raw/main/models/GTSRB/Brevitas_CNV1W1A/cnv_1w1a_gtsrb.onnx
+wget https://github.com/Xilinx/finn-examples/releases/download/v0.0.7a/onnx-models-gtsrb.zip
+unzip -j onnx-models-gtsrb.zip
diff --git a/build/kws/build.py b/build/kws/build.py
index 7ffd513..b73cee9 100644
--- a/build/kws/build.py
+++ b/build/kws/build.py
@@ -67,7 +67,7 @@ def step_preprocess(model: ModelWrapper, cfg: DataflowBuildConfig):
     build_cfg.VerificationStepType.FOLDED_HLS_CPPSIM,
 ]
 
-model_name = "MLP_W3A3_python_speech_features_pre-processing_QONNX"
+model_name = "MLP_W3A3_python_speech_features_pre-processing_QONNX_opset-11"
 model_file = "models/" + model_name + ".onnx"
 
 # Change the ONNX opset from version 9 to 11, which adds support for the TopK node
diff --git a/build/kws/models/download-model.sh b/build/kws/models/download-model.sh
index 50c2314..ba63cf7 100755
--- a/build/kws/models/download-model.sh
+++ b/build/kws/models/download-model.sh
@@ -29,4 +29,5 @@
 
 # Download validation data and model
 wget https://github.com/Xilinx/finn-examples/releases/download/kws/python_speech_preprocessing_all_validation_KWS_data.npz
-wget https://github.com/Xilinx/finn-examples/releases/download/kws/MLP_W3A3_python_speech_features_pre-processing_QONNX.onnx
+wget https://github.com/Xilinx/finn-examples/releases/download/v0.0.7a/onnx-models-kws.zip
+unzip -j onnx-models-kws.zip
diff --git a/build/mobilenet-v1/build.py b/build/mobilenet-v1/build.py
index 7f6974c..59b655d 100644
--- a/build/mobilenet-v1/build.py
+++ b/build/mobilenet-v1/build.py
@@ -148,7 +148,7 @@ def select_build_steps(platform):
         specialize_layers_config_file="specialize_layers_config/%s_specialize_layers.json"
         % platform_name,
     )
-    model_file = "models/%s_pre_post_tidy.onnx" % model_name
+    model_file = "models/%s_pre_post_tidy_opset-11.onnx" % model_name
     build.build_dataflow_cfg(model_file, cfg)
 
     # copy bitfiles and runtime weights into release dir if found
diff --git a/build/mobilenet-v1/models/download-model.sh b/build/mobilenet-v1/models/download-model.sh
index fe54a6c..5caa91b 100755
--- a/build/mobilenet-v1/models/download-model.sh
+++ b/build/mobilenet-v1/models/download-model.sh
@@ -1,4 +1,4 @@
 #!/bin/sh
 
-wget https://github.com/Xilinx/finn-examples/releases/download/v0.0.1a/onnx-models-mobilenetv1.zip
-unzip onnx-models-mobilenetv1.zip
+wget https://github.com/Xilinx/finn-examples/releases/download/v0.0.7a/onnx-models-mobilenetv1.zip
+unzip -j onnx-models-mobilenetv1.zip
diff --git a/build/resnet50/models/download-model.sh b/build/resnet50/models/download-model.sh
index 0521649..80c82bf 100755
--- a/build/resnet50/models/download-model.sh
+++ b/build/resnet50/models/download-model.sh
@@ -1,4 +1,4 @@
 #!/bin/sh
 
-wget https://github.com/Xilinx/finn-examples/releases/download/v0.0.1a/onnx-models-resnet50.zip
-unzip onnx-models-resnet50.zip
+wget https://github.com/Xilinx/finn-examples/releases/download/v0.0.7a/onnx-models-resnet50.zip
+unzip -j onnx-models-resnet50.zip
diff --git a/build/vgg10-radioml/models/download-model.sh b/build/vgg10-radioml/models/download-model.sh
index 1d6bae6..3dfe24c 100755
--- a/build/vgg10-radioml/models/download-model.sh
+++ b/build/vgg10-radioml/models/download-model.sh
@@ -1,3 +1,4 @@
 #!/bin/sh
 
-wget https://github.com/Xilinx/finn-examples/releases/download/radioml/radioml_w4a4_small_tidy.onnx
+wget https://github.com/Xilinx/finn-examples/releases/download/v0.0.7a/onnx-models-radioml.zip
+unzip -j onnx-models-radioml.zip
diff --git a/finn_examples/bitfiles/bitfiles.zip.link b/finn_examples/bitfiles/bitfiles.zip.link
index c8d3762..bea9c49 100644
--- a/finn_examples/bitfiles/bitfiles.zip.link
+++ b/finn_examples/bitfiles/bitfiles.zip.link
@@ -1,22 +1,22 @@
 {
     "Pynq-Z1": {
-        "url": "https://github.com/Xilinx/finn-examples/releases/download/v0.0.6/Pynq-Z1.zip",
-        "md5sum": "54b4691e5195ff10fb0c5339b78d6ea5"
+        "url": "https://github.com/Xilinx/finn-examples/releases/download/v0.0.7a/Pynq-Z1.zip",
+        "md5sum": "402ab9dfa4612ae340715bf80e4717bd"
     },
     "Ultra96": {
-        "url": "https://github.com/Xilinx/finn-examples/releases/download/v0.0.6/Ultra96.zip",
-        "md5sum": "a297d1f5657f624ffce16c901f1f3f30"
+        "url": "https://github.com/Xilinx/finn-examples/releases/download/v0.0.7a/Ultra96.zip",
+        "md5sum": "f777342ebc7f589950cf96843aeb6781"
     },
     "ZCU104": {
-        "url": "https://github.com/Xilinx/finn-examples/releases/download/v0.0.6/ZCU104.zip",
-        "md5sum": "17dd6ba3d24002275b6622ae0c098378"
+        "url": "https://github.com/Xilinx/finn-examples/releases/download/v0.0.7a/ZCU104.zip",
+        "md5sum": "0e701e154a78e050cea69d2c9efefccf"
     },
     "xilinx_u250_xdma_201830_2": {
         "url": "https://github.com/Xilinx/finn-examples/releases/download/rn50-u250/xilinx_u250_xdma_201830_2.zip",
         "md5sum": "042cc5602c8a39d7541f1d79946c0b68"
     },
     "xilinx_u250_gen3x16_xdma_2_1_202010_1": {
-        "url": "https://github.com/Xilinx/finn-examples/releases/download/v0.0.6/xilinx_u250_gen3x16_xdma_2_1_202010_1.zip",
-        "md5sum": "59a61f233376ab0e340835034db50449"
+        "url": "https://github.com/Xilinx/finn-examples/releases/download/v0.0.7a/xilinx_u250_gen3x16_xdma_2_1_202010_1.zip",
+        "md5sum": "59d0089ebc135b0509c8ee5a8666b88f"
     }
 }
diff --git a/finn_examples/models.py b/finn_examples/models.py
index 3a34a61..0db17ec 100644
--- a/finn_examples/models.py
+++ b/finn_examples/models.py
@@ -356,7 +356,7 @@ def mobilenetv1_w4a4_imagenet(target_platform=None, bitfile_path=None, rt_weight
 def resnet50_w1a2_imagenet(target_platform=None, bitfile_path=None, rt_weights_path=None):
     target_platform = resolve_target_platform(target_platform)
     driver_mode = get_driver_mode()
-    model_name = "resnet50-w1a2"
+    model_name = "resnet50_w1a2"
     filename = find_bitfile(model_name, target_platform, bitfile_path)
     return FINNExampleOverlay(
         filename,
@@ -393,6 +393,6 @@ def mlp_w2a2_unsw_nb15(target_platform=None, bitfile_path=None):
 def cnv_w1a1_gtsrb(target_platform=None, bitfile_path=None):
     target_platform = resolve_target_platform(target_platform)
     driver_mode = get_driver_mode()
-    model_name = "cnv-gtsrb-w1a1"
+    model_name = "cnv_1w1a_gtsrb"
     filename = find_bitfile(model_name, target_platform, bitfile_path)
     return FINNExampleOverlay(filename, driver_mode, _gtsrb_cnv_io_shape_dict)
diff --git a/finn_examples/notebooks/7_traffic_sign_recognition_gtsrb.ipynb b/finn_examples/notebooks/7_traffic_sign_recognition_gtsrb.ipynb
index a8c49fd..003ff1b 100644
--- a/finn_examples/notebooks/7_traffic_sign_recognition_gtsrb.ipynb
+++ b/finn_examples/notebooks/7_traffic_sign_recognition_gtsrb.ipynb
@@ -200,7 +200,7 @@
     "ok = 0\n",
     "nok = 0\n",
     "for i in range(n_batches):\n",
-    "    ibuf_normal = batch_imgs[i].reshape(accel.ibuf_packed_device.shape)\n",
+    "    ibuf_normal = batch_imgs[i].reshape(np.shape(accel.ibuf_packed_device))\n",
     "    exp = batch_labels[i]\n",
     "    # to avoid the slower software implementation during data unpacking,\n",
     "    # we make manual calls to buffer copies and execute_on_buffers\n",
diff --git a/pyproject.toml b/pyproject.toml
index 7c60c34..8f218ea 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,5 +1,5 @@
 [build-system]
-requires = ["setuptools>=45", "setuptools_scm[toml]>=6.2", "wheel", "pynq>=2.5.1"]
+requires = ["setuptools>=45", "setuptools_scm[toml]>=6.2", "wheel", "pynq==3.0.1", "ipython"]
 
 [tool.setuptools_scm]
 write_to = "finn_examples/_version.py"
diff --git a/setup.py b/setup.py
index 1775a5d..1e55402 100644
--- a/setup.py
+++ b/setup.py
@@ -116,16 +116,16 @@ def extend_package(path):
     python_requires=">=3.5.2",
     # keeping 'setup_requires' only for readability - relying on
     # pyproject.toml and PEP 517/518
-    setup_requires=["pynq>=2.5.1", "setuptools_scm"],
+    setup_requires=["pynq==3.0.1", "setuptools_scm"],
     install_requires=[
-        "pynq>=2.5.1",
+        "pynq==3.0.1",
         "bitstring>=3.1.7",
-        "numpy",
+        "numpy==1.21.5",
         "finn-dataset_loading==0.0.5",  # noqa
     ],
     extras_require={
         ':python_version<"3.6"': ["matplotlib<3.1", "ipython==7.9"],
-        ':python_version>="3.6"': ["matplotlib"],
+        ':python_version>="3.6"': ["matplotlib", "ipython==8.9.0"],
     },
     entry_points={"pynq.notebooks": ["finn_examples = {}.notebooks".format(module_name)]},
     cmdclass={"build_py": build_py, "unzip_overlays": _unzip_overlays},