diff --git a/src/brevitas/graph/gpxq.py b/src/brevitas/graph/gpxq.py
index c38a15712..b85ac1188 100644
--- a/src/brevitas/graph/gpxq.py
+++ b/src/brevitas/graph/gpxq.py
@@ -266,7 +266,7 @@ def get_quant_weights(self, i, i1, permutation_list):
             subtensor_slice_list = [None, (index, index + 1)]
             q = self.layer.quant_weight(
                 subtensor_slice_list=subtensor_slice_list,
-                quant_input=self.quant_input).value.unsqueeze(0)  # [1, OC, 1]
+                quant_input=self.quant_metadata).value.unsqueeze(0)  # [1, OC, 1]
         elif isinstance(self.layer, SUPPORTED_CONV_OP):
             # For depthwise and ConvTranspose we fall back to quantizing the entire martix.
             # For all other cases, we create a mask that represent the slicing we will perform on the weight matrix
@@ -274,7 +274,7 @@ def get_quant_weights(self, i, i1, permutation_list):
             if self.groups > 1 or (self.groups == 1 and isinstance(
                     self.layer, (qnn.QuantConvTranspose1d, qnn.QuantConvTranspose2d))):
 
-                quant_weight = self.layer.quant_weight(quant_input=self.quant_input)
+                quant_weight = self.layer.quant_weight(quant_input=self.quant_metadata)
                 quant_weight = quant_weight.value
 
                 if isinstance(self.layer, (qnn.QuantConvTranspose1d, qnn.QuantConvTranspose2d)):
@@ -299,7 +299,7 @@ def get_quant_weights(self, i, i1, permutation_list):
                 index_2d_to_nd.insert(0, None)
                 q = self.layer.quant_weight(
                     subtensor_slice_list=index_2d_to_nd,
-                    quant_input=self.quant_input).value.flatten(1)  # [OC, 1]
+                    quant_input=self.quant_metadata).value.flatten(1)  # [OC, 1]
                 q = q.unsqueeze(0)  # [1, OC, 1]
         # We need to remove the last dim
         q = q.squeeze(2)  # [groups, OC/groups] or [1, OC]