diff --git a/src/prototorch/models/callbacks.py b/src/prototorch/models/callbacks.py
index b58ccc1..e0d8fae 100644
--- a/src/prototorch/models/callbacks.py
+++ b/src/prototorch/models/callbacks.py
@@ -10,7 +10,8 @@
 from .extras import ConnectionTopology
 
 if TYPE_CHECKING:
-    from prototorch.models import GLVQ, GrowingNeuralGas
+    from prototorch.models.glvq import GLVQ
+    from prototorch.models.knn import GrowingNeuralGas
 
 
 class PruneLoserPrototypes(pl.Callback):
@@ -61,7 +62,7 @@ def on_train_epoch_end(self, trainer, pl_module: "GLVQ"):
                                               return_counts=True)
                 distribution = dict(zip(labels.tolist(), counts.tolist()))
 
-                logging.info(f"Re-adding pruned prototypes...")
+                logging.info("Re-adding pruned prototypes...")
                 logging.debug(f"distribution={distribution}")
 
                 pl_module.add_prototypes(
diff --git a/src/prototorch/models/glvq.py b/src/prototorch/models/glvq.py
index 4328b10..941f442 100644
--- a/src/prototorch/models/glvq.py
+++ b/src/prototorch/models/glvq.py
@@ -1,13 +1,15 @@
 """Models based on the GLVQ framework."""
 
 import torch
+from numpy.typing import NDArray
 from prototorch.core.competitions import wtac
 from prototorch.core.distances import (
+    ML_omega_distance,
     lomega_distance,
     omega_distance,
     squared_euclidean_distance,
 )
-from prototorch.core.initializers import EyeLinearTransformInitializer
+from prototorch.core.initializers import LLTI, EyeLinearTransformInitializer
 from prototorch.core.losses import (
     GLVQLoss,
     lvq1_loss,
@@ -15,7 +17,7 @@
 )
 from prototorch.core.transforms import LinearTransform
 from prototorch.nn.wrappers import LambdaLayer, LossLayer
-from torch.nn.parameter import Parameter
+from torch.nn import Parameter, ParameterList
 
 from .abstract import ImagePrototypesMixin, SupervisedPrototypeModel
 from .extras import ltangent_distance, orthogonalization
@@ -175,6 +177,7 @@ class GRLVQ(SiameseGLVQ):
     TODO Make a RelevanceLayer. `bb_lr` is ignored otherwise.
 
     """
+
     _relevances: torch.Tensor
 
     def __init__(self, hparams, **kwargs):
@@ -229,6 +232,51 @@ def lambda_matrix(self):
         return lam.detach().cpu()
 
 
+class GMLMLVQ(GLVQ):
+    """Generalized Multi-Layer Matrix Learning Vector Quantization.
+    Masks are applied to the omega layers to achieve sparsity and constrain
+    learning to certain items of each omega.
+
+    Implemented as a regular GLVQ network that simply uses a different distance
+    function. This makes it easier to implement a localized variant.
+    """
+
+    # Parameters
+    _omegas: list[torch.Tensor]
+    masks: list[torch.Tensor]
+
+    def __init__(self, hparams, **kwargs):
+        distance_fn = kwargs.pop("distance_fn", ML_omega_distance)
+        super().__init__(hparams, distance_fn=distance_fn, **kwargs)
+
+        # Additional parameters
+        self._masks = ParameterList([
+            Parameter(mask, requires_grad=False)
+            for mask in kwargs.get("masks")
+        ])
+        self._omegas = ParameterList(
+            [LLTI(mask).generate(1, 1) for mask in self._masks])
+
+    @property
+    def omega_matrices(self):
+        return [_omega.detach().cpu() for _omega in self._omegas]
+
+    @property
+    def lambda_matrix(self):
+        # TODO update to respective lambda calculation rules.
+        omega = self._omega.detach()  # (input_dim, latent_dim)
+        lam = omega @ omega.T
+        return lam.detach().cpu()
+
+    def compute_distances(self, x):
+        protos, _ = self.proto_layer()
+        distances = self.distance_layer(x, protos, self._omegas, self._masks)
+        return distances
+
+    def extra_repr(self):
+        return f"(omegas): (shapes: {[tuple(_omega.shape) for _omega in self._omegas]})"
+
+
 class GMLVQ(GLVQ):
     """Generalized Matrix Learning Vector Quantization.