diff --git a/src/brevitas_examples/imagenet_classification/ptq/ptq_common.py b/src/brevitas_examples/imagenet_classification/ptq/ptq_common.py
index 62752f1d0..9f00f0a62 100644
--- a/src/brevitas_examples/imagenet_classification/ptq/ptq_common.py
+++ b/src/brevitas_examples/imagenet_classification/ptq/ptq_common.py
@@ -2,6 +2,7 @@
 # SPDX-License-Identifier: BSD-3-Clause
 
 from copy import deepcopy
+import math
 
 import torch
 import torch.backends.cudnn as cudnn
@@ -153,6 +154,20 @@ def quantize_model(
     weight_scale_type = scale_factor_type
     act_scale_type = scale_factor_type
 
+    # We check all of the provided values are positive integers
+    check_positive_int(
+        weight_bit_width,
+        act_bit_width,
+        bias_bit_width,
+        layerwise_first_last_bit_width,
+        layerwise_first_last_mantissa_bit_width,
+        layerwise_first_last_exponent_bit_width,
+        weight_mantissa_bit_width,
+        weight_exponent_bit_width,
+        act_mantissa_bit_width,
+        act_exponent_bit_width,
+    )
+
     weight_quant_format = quant_format
     act_quant_format = quant_format
 
@@ -535,3 +550,12 @@ def apply_learned_round_learning(
             pbar.set_description(
                 "loss = {:.4f}, rec_loss = {:.4f}, round_loss = {:.4f}, b = {:.4f}".format(
                     loss, rec_loss, round_loss, b))
+
+
+def check_positive_int(*args):
+    """
+    We check that every inputted value is positive, and an integer.
+    """
+    for arg in args:
+        assert arg > 0.0
+        assert math.isclose(arg % 1, 0.0)