diff --git a/src/brevitas/export/onnx/qonnx/handler.py b/src/brevitas/export/onnx/qonnx/handler.py
index 34a4dbd9f..6aa17eebc 100644
--- a/src/brevitas/export/onnx/qonnx/handler.py
+++ b/src/brevitas/export/onnx/qonnx/handler.py
@@ -36,7 +36,7 @@ def prepare_for_export(self, module):
                 'bit_width': module.bit_width(),
                 'narrow_range': module.is_narrow_range,
                 'signed': module.is_signed,
-                'rounding_mode': module.rounding_mode}
+                'rounding_mode': module.rounding_mode.upper()}
 
     def symbolic_execution(self, x: Tensor):
         scale = self.symbolic_kwargs['scale']
@@ -116,7 +116,7 @@ class BrevitasTruncQuantProxyHandler(ONNXBaseHandler):
 
     def prepare_for_export(self, module: TruncQuantProxyFromInjector):
         self.symbolic_kwargs = {
-            'output_bit_width': module.bit_width(), 'rounding_mode': module.rounding_mode}
+            'output_bit_width': module.bit_width(), 'rounding_mode': module.rounding_mode.upper()}
 
     def symbolic_execution(
             self, x: Tensor, scale: Tensor, zero_point: Tensor, input_bit_width: Tensor,
diff --git a/src/brevitas_examples/bnn_pynq/bnn_pynq_train.py b/src/brevitas_examples/bnn_pynq/bnn_pynq_train.py
index 06f8ea875..fd4281a3c 100644
--- a/src/brevitas_examples/bnn_pynq/bnn_pynq_train.py
+++ b/src/brevitas_examples/bnn_pynq/bnn_pynq_train.py
@@ -69,6 +69,10 @@ def parse_args(args):
     parser.add_argument("--network", default="LFC_1W1A", type=str, help="neural network")
     parser.add_argument("--pretrained", action='store_true', help="Load pretrained model")
     parser.add_argument("--strict", action='store_true', help="Strict state dictionary loading")
+    parser.add_argument(
+        "--state_dict_to_pth",
+        action='store_true',
+        help="Saves a model state_dict into a pth and then exits")
     return parser.parse_args(args)
 
 
diff --git a/src/brevitas_examples/bnn_pynq/cfg/resnet18_3w3a.ini b/src/brevitas_examples/bnn_pynq/cfg/resnet18_3w3a.ini
deleted file mode 100644
index 59ce49871..000000000
--- a/src/brevitas_examples/bnn_pynq/cfg/resnet18_3w3a.ini
+++ /dev/null
@@ -1,8 +0,0 @@
-[MODEL]
-ARCH: RESNET18
-DATASET: CIFAR10
-NUM_CLASSES: 10
-
-[QUANT]
-WEIGHT_BIT_WIDTH: 3
-ACT_BIT_WIDTH: 3
diff --git a/src/brevitas_examples/bnn_pynq/cfg/resnet18_4w4a.ini b/src/brevitas_examples/bnn_pynq/cfg/resnet18_4w4a.ini
index f6ebe2404..b695e522e 100644
--- a/src/brevitas_examples/bnn_pynq/cfg/resnet18_4w4a.ini
+++ b/src/brevitas_examples/bnn_pynq/cfg/resnet18_4w4a.ini
@@ -2,6 +2,7 @@
 ARCH: RESNET18
 DATASET: CIFAR10
 NUM_CLASSES: 10
+PRETRAINED_URL: https://github.com/Xilinx/brevitas/releases/download/bnn_pynq-r2/resnet18_4w4a-a172f334.pth
 
 [QUANT]
 WEIGHT_BIT_WIDTH: 4
diff --git a/src/brevitas_examples/bnn_pynq/models/__init__.py b/src/brevitas_examples/bnn_pynq/models/__init__.py
index b958d510f..33753c7ab 100644
--- a/src/brevitas_examples/bnn_pynq/models/__init__.py
+++ b/src/brevitas_examples/bnn_pynq/models/__init__.py
@@ -104,11 +104,6 @@ def lfc_1w2a(pretrained=True):
     return model
 
 
-def resnet18_4w4a(pretrained=False):
+def resnet18_4w4a(pretrained=True):
     model, _ = model_with_cfg('resnet18_4w4a', pretrained)
     return model
-
-
-def resnet18_3w3a(pretrained=False):
-    model, _ = model_with_cfg('resnet18_3w3a', pretrained)
-    return model
diff --git a/src/brevitas_examples/bnn_pynq/trainer.py b/src/brevitas_examples/bnn_pynq/trainer.py
index d9b1283a1..78c1db97e 100644
--- a/src/brevitas_examples/bnn_pynq/trainer.py
+++ b/src/brevitas_examples/bnn_pynq/trainer.py
@@ -2,6 +2,7 @@
 # SPDX-License-Identifier: BSD-3-Clause
 
 from datetime import datetime
+from hashlib import sha256
 import os
 import random
 import time
@@ -135,6 +136,19 @@ def __init__(self, args):
             model_state_dict = package['state_dict']
             model.load_state_dict(model_state_dict, strict=args.strict)
 
+        if args.state_dict_to_pth:
+            state_dict = model.state_dict()
+            name = args.network.lower()
+            path = os.path.join(self.checkpoints_dir_path, name)
+            torch.save(state_dict, path)
+            with open(path, "rb") as f:
+                bytes = f.read()
+                readable_hash = sha256(bytes).hexdigest()[:8]
+            new_path = path + '-' + readable_hash + '.pth'
+            os.rename(path, new_path)
+            self.logger.info("Saving checkpoint model to {}".format(new_path))
+            exit(0)
+
         if args.gpus is not None and len(args.gpus) == 1:
             model = model.to(device=self.device)
         if args.gpus is not None and len(args.gpus) > 1: