diff --git a/python/mlx/optimizers.py b/python/mlx/optimizers.py
index 4fb48935b..9a77328d3 100644
--- a/python/mlx/optimizers.py
+++ b/python/mlx/optimizers.py
@@ -152,3 +152,42 @@ class Adam(Optimizer):
         state["v"] = v
 
         return parameter - lr * m / (mx.sqrt(v) + eps)
+
+
+class AdamW(Adam):
+    r"""Implementation of the AdamW optimizer [1].
+
+    Following the above convention, in contrast with [1], we do not use bias
+    correction in the first and second moments for AdamW. We update the weights 
+    with a weight_decay (λ) value:
+
+    .. math::
+
+        m_{t+1} &= \beta_1 m_t + (1 - \beta_1) g_t \\
+        v_{t+1} &= \beta_2 v_t + (1 - \beta_2) g_t^2 \\
+        w_{t+1} &= w_t - \alpha (\frac{m_{t+1}}{\sqrt{v_{t+1} + \epsilon}} + \lambda w_t)
+
+    [1]: Loshchilov, I. and Hutter, F., 2019. Decoupled weight decay 
+    regularization. ICLR 2019.
+    """
+
+    def __init__(
+        self,
+        learning_rate: float,
+        betas: List[float] = [0.9, 0.999],
+        eps: float = 1e-8,
+        weight_decay: float = 0.01,
+    ):
+        super().__init__(learning_rate=learning_rate, betas=betas, eps=eps)
+        self.weight_decay = weight_decay
+
+    def apply_single(
+        self, gradient: mx.array, parameter: mx.array, state: OptimizerState
+    ):
+        """Performs the AdamW parameter update by modifying the parameters 
+        passed into Adam.
+        """
+
+        return super().apply_single(
+            gradient, parameter * (1 - self.learning_rate * self.weight_decay), state
+        )