diff --git a/prototorch/models/abstract.py b/prototorch/models/abstract.py
index ca21908..a80189e 100644
--- a/prototorch/models/abstract.py
+++ b/prototorch/models/abstract.py
@@ -37,33 +37,3 @@ class PrototypeImageModel(pl.LightningModule):
         if return_channels_last:
             grid = grid.permute((1, 2, 0))
         return grid.cpu()
-
-
-class SiamesePrototypeModel(pl.LightningModule):
-    def configure_optimizers(self):
-        proto_opt = self.optimizer(self.proto_layer.parameters(),
-                                   lr=self.hparams.proto_lr)
-        if list(self.backbone.parameters()):
-            # only add an optimizer is the backbone has trainable parameters
-            # otherwise, the next line fails
-            bb_opt = self.optimizer(self.backbone.parameters(),
-                                    lr=self.hparams.bb_lr)
-            return proto_opt, bb_opt
-        else:
-            return proto_opt
-
-    def predict_latent(self, x, map_protos=True):
-        """Predict `x` assuming it is already embedded in the latent space.
-
-        Only the prototypes are embedded in the latent space using the
-        backbone.
-
-        """
-        self.eval()
-        with torch.no_grad():
-            protos, plabels = self.proto_layer()
-            if map_protos:
-                protos = self.backbone(protos)
-            d = self.distance_fn(x, protos)
-            y_pred = wtac(d, plabels)
-        return y_pred
diff --git a/prototorch/models/cbc.py b/prototorch/models/cbc.py
index 64b48c1..3862eba 100644
--- a/prototorch/models/cbc.py
+++ b/prototorch/models/cbc.py
@@ -5,8 +5,7 @@ from prototorch.components.components import Components
 from prototorch.functions.distances import euclidean_distance
 from prototorch.functions.similarities import cosine_similarity
 
-from .abstract import (AbstractPrototypeModel, PrototypeImageModel,
-                       SiamesePrototypeModel)
+from .abstract import AbstractPrototypeModel, PrototypeImageModel
 from .glvq import SiameseGLVQ
 
 
diff --git a/prototorch/models/glvq.py b/prototorch/models/glvq.py
index e34d023..781b809 100644
--- a/prototorch/models/glvq.py
+++ b/prototorch/models/glvq.py
@@ -9,8 +9,7 @@ from prototorch.functions.helper import get_flat
 from prototorch.functions.losses import (_get_dp_dm, _get_matcher, glvq_loss,
                                          lvq1_loss, lvq21_loss)
 
-from .abstract import (AbstractPrototypeModel, PrototypeImageModel,
-                       SiamesePrototypeModel)
+from .abstract import AbstractPrototypeModel, PrototypeImageModel
 
 
 class GLVQ(AbstractPrototypeModel):
@@ -130,7 +129,7 @@ class GLVQ(AbstractPrototypeModel):
         return f"{super_repr}"
 
 
-class SiameseGLVQ(SiamesePrototypeModel, GLVQ):
+class SiameseGLVQ(GLVQ):
     """GLVQ in a Siamese setting.
 
     GLVQ model that applies an arbitrary transformation on the inputs and the
@@ -148,6 +147,18 @@ class SiameseGLVQ(SiamesePrototypeModel, GLVQ):
         self.both_path_gradients = both_path_gradients
         self.distance_fn = kwargs.get("distance_fn", sed)
 
+    def configure_optimizers(self):
+        proto_opt = self.optimizer(self.proto_layer.parameters(),
+                                   lr=self.hparams.proto_lr)
+        if list(self.backbone.parameters()):
+            # only add an optimizer is the backbone has trainable parameters
+            # otherwise, the next line fails
+            bb_opt = self.optimizer(self.backbone.parameters(),
+                                    lr=self.hparams.bb_lr)
+            return proto_opt, bb_opt
+        else:
+            return proto_opt
+
     def _forward(self, x):
         protos, _ = self.proto_layer()
         latent_x = self.backbone(x)
@@ -157,6 +168,22 @@ class SiameseGLVQ(SiamesePrototypeModel, GLVQ):
         distances = self.distance_fn(latent_x, latent_protos)
         return distances
 
+    def predict_latent(self, x, map_protos=True):
+        """Predict `x` assuming it is already embedded in the latent space.
+
+        Only the prototypes are embedded in the latent space using the
+        backbone.
+
+        """
+        self.eval()
+        with torch.no_grad():
+            protos, plabels = self.proto_layer()
+            if map_protos:
+                protos = self.backbone(protos)
+            d = self.distance_fn(x, protos)
+            y_pred = wtac(d, plabels)
+        return y_pred
+
 
 class GRLVQ(SiameseGLVQ):
     """Generalized Relevance Learning Vector Quantization."""