diff --git a/src/transformers/models/mixtral/modeling_mixtral.py b/src/transformers/models/mixtral/modeling_mixtral.py
index 6788dcde8c3e22..414ffe671ae590 100644
--- a/src/transformers/models/mixtral/modeling_mixtral.py
+++ b/src/transformers/models/mixtral/modeling_mixtral.py
@@ -663,10 +663,10 @@ def __init__(self, config: MixtralConfig):
 
         self.act_fn = ACT2FN[config.hidden_act]
 
-    def forward(self, hidden_states, routing_weights):
+    def forward(self, hidden_states):
         current_hidden_states = self.act_fn(self.w1(hidden_states)) * self.w3(hidden_states)
         current_hidden_states = self.w2(current_hidden_states)
-        return routing_weights * current_hidden_states
+        return current_hidden_states
 
 
 MISTRAL_ATTENTION_CLASSES = {
@@ -736,7 +736,7 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
             # the current expert. We need to make sure to multiply the output hidden
             # states by `routing_weights` on the corresponding tokens (top-1 and top-2)
             current_state = hidden_states[None, top_x_list].reshape(-1, hidden_dim)
-            current_hidden_states = expert_layer(current_state, routing_weights[top_x_list, idx_list, None])
+            current_hidden_states = expert_layer(current_state) * routing_weights[top_x_list, idx_list, None]
 
             # However `index_add_` only support torch tensors for indexing so we'll use
             # the `top_x` tensor here.