diff --git a/equimo/layers/norm.py b/equimo/layers/norm.py
index 837c715..2023361 100644
--- a/equimo/layers/norm.py
+++ b/equimo/layers/norm.py
@@ -58,12 +58,10 @@ class LayerScale(eqx.Module):
     dampening the contribution of each layer.
 
     Attributes:
-        init_values: Initial scale value (static)
         gamma: Learnable scale parameters of size dim
     """
 
-    init_values: float = eqx.field(static=True)
-    gamma: Optional[Float[Array, "dim"]]
+    gamma: Float[Array, "dim"]
 
     def __init__(self, dim: int, init_values: float):
         """Initialize LayerScale.
@@ -72,8 +70,7 @@ def __init__(self, dim: int, init_values: float):
             dim: Dimension of the input features
             init_values: Initial value for all scaling factors
         """
-        self.init_values = init_values
-        self.gamma = jnp.repeat(self.init_values, dim)
+        self.gamma = jnp.repeat(init_values, dim)
 
     def __call__(self, x: Float[Array, "dim"]):
         """Apply layer scaling to input tensor.
@@ -87,6 +84,49 @@ def __call__(self, x: Float[Array, "dim"]):
         return x * self.gamma
 
 
+class DyT(eqx.Module):
+    """Dynamic Tanh layear.
+
+    This layer implements the DyT layer introduced in the Transformer
+    without Normalization paper[1].
+
+    Attributes:
+        init_values: Initial scale value (static)
+        gamma: Learnable scale parameters of size dim
+
+    References:
+        [1]. Zhu, et al., Transformers without Normalization. 2025.
+             https://arxiv.org/abs/2503.10622
+    """
+
+    alpha: Float[Array, "dim"]
+    weight: Float[Array, "dim"]
+    bias: Float[Array, "dim"]
+
+    def __init__(self, dim: int, alpha_init_value: float = 0.5):
+        """Initialize DyT.
+
+        Args:
+            dim: Dimension of the input features
+            alpha_init_value: Initial value for the scaling factor
+        """
+        self.alpha = jnp.repeat(alpha_init_value, dim)
+        self.weight = jnp.ones(dim)
+        self.bias = jnp.zeros(dim)
+
+    def __call__(self, x: Float[Array, "dim"]):
+        """Apply dynamic tanh to input tensor.
+
+        Args:
+            x: Input tensor of shape (dim,)
+
+        Returns:
+            Scaled tensor of same shape as input
+        """
+        x = jnp.tanh(self.alpha * x)
+        return x * self.weight + self.bias
+
+
 def get_norm(module: str | eqx.Module) -> eqx.Module:
     """Get an `eqx.Module` from its common name.
 
@@ -107,5 +147,7 @@ def get_norm(module: str | eqx.Module) -> eqx.Module:
             return RMSNormGated
         case "layerscale":
             return LayerScale
+        case "dynamictanh":
+            return DyT
         case _:
             raise ValueError(f"Got an unknown module string: {module}")