diff --git a/i6_models/primitives/weight_dropout.py b/i6_models/primitives/weight_dropout.py
new file mode 100644
index 00000000..d1c80d12
--- /dev/null
+++ b/i6_models/primitives/weight_dropout.py
@@ -0,0 +1,56 @@
+__all__ = ["WeightDrop", "WeightDropLinear"]
+
+import torch
+from torch.nn import Parameter
+
+from typing import List
+
+
+def _weight_drop(module: torch.nn.Module, weights: List[str], dropout: float = 0.0):
+    for name_w in weights:
+        w = getattr(module, name_w)
+        del module._parameters[name_w]
+        module.register_parameter(name_w + "_raw", Parameter(w))
+
+    original_module_forward = module.forward
+
+    def forward(*args, **kwargs):
+        for name_w in weights:
+            raw_w = getattr(module, name_w + "_raw")
+            w = torch.nn.functional.dropout(raw_w, p=dropout, training=module.training)
+            setattr(module, name_w, w)
+
+        return original_module_forward(*args, **kwargs)
+
+    setattr(module, "forward", forward)
+
+
+class WeightDrop(torch.nn.Module):
+    """
+    Apply dropout on weights of a given nn.module
+    implementation c.f. https://pytorchnlp.readthedocs.io/en/latest/_modules/torchnlp/nn/weight_drop.html
+    """
+
+    def __init__(self, module: torch.nn.Module, weights: List[str], dropout: float = 0.0):
+        """
+        Attributes:
+            module:
+        """
+        super(WeightDrop, self).__init__()
+        _weight_drop(module, weights, dropout)
+        self.forward = module.forward
+
+
+class WeightDropLinear(torch.nn.Linear):
+    """
+    Wrapper around :class:`torch.nn.Linear` that apply dropout on the weights
+    """
+
+    def __init__(self, *args, weight_dropout: float = 0.0, **kwargs):
+        """
+        Attributes:
+            weight_dropout: the dropout probability
+        """
+        super().__init__(*args, **kwargs)
+        weights = ["weight"]
+        _weight_drop(self, weights, weight_dropout)