diff --git a/vllm/model_executor/layers/quantization/mxfp4.py b/vllm/model_executor/layers/quantization/mxfp4.py
index b6d7bc5d5cccd..068af027398ba 100644
--- a/vllm/model_executor/layers/quantization/mxfp4.py
+++ b/vllm/model_executor/layers/quantization/mxfp4.py
@@ -109,55 +109,74 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
         self.intermediate_size = intermediate_size_per_partition_after_pad
         self.hidden_size = hidden_size
         # Fused gate_up_proj (column parallel)
-        w13_weight = torch.nn.Parameter(torch.zeros(
-            num_experts,
-            2 * intermediate_size_per_partition_after_pad,
-            hidden_size // 2,
-            dtype=weight_dtype),
-                                        requires_grad=False)
+        w13_weight = torch.nn.Parameter(
+            torch.zeros(
+                num_experts,
+                2 * intermediate_size_per_partition_after_pad,
+                hidden_size // 2,
+                dtype=weight_dtype,
+            ),
+            requires_grad=False,
+        )
         layer.register_parameter("w13_weight", w13_weight)
         set_weight_attrs(w13_weight, extra_weight_attrs)
 
-        w13_weight_scale = torch.nn.Parameter(torch.zeros(
-            num_experts,
-            2 * intermediate_size_per_partition_after_pad,
-            hidden_size // mxfp4_block,
-            dtype=scale_dtype),
-                                              requires_grad=False)
+        w13_weight_scale = torch.nn.Parameter(
+            torch.zeros(
+                num_experts,
+                2 * intermediate_size_per_partition_after_pad,
+                hidden_size // mxfp4_block,
+                dtype=scale_dtype,
+            ),
+            requires_grad=False,
+        )
         layer.register_parameter("w13_weight_scale", w13_weight_scale)
         set_weight_attrs(w13_weight_scale, extra_weight_attrs)
 
-        w13_bias = torch.nn.Parameter(torch.zeros(
-            num_experts,
-            2 * intermediate_size_per_partition_after_pad,
-            dtype=torch.bfloat16),
-                                      requires_grad=False)
+        w13_bias = torch.nn.Parameter(
+            torch.zeros(
+                num_experts,
+                2 * intermediate_size_per_partition_after_pad,
+                dtype=torch.bfloat16,
+            ),
+            requires_grad=False,
+        )
         layer.register_parameter("w13_bias", w13_bias)
         set_weight_attrs(w13_bias, extra_weight_attrs)
 
         # down_proj (row parallel)
-        w2_weight = torch.nn.Parameter(torch.zeros(
-            num_experts,
-            hidden_size,
-            intermediate_size_per_partition_after_pad // 2,
-            dtype=weight_dtype),
-                                       requires_grad=False)
+        w2_weight = torch.nn.Parameter(
+            torch.zeros(
+                num_experts,
+                hidden_size,
+                intermediate_size_per_partition_after_pad // 2,
+                dtype=weight_dtype,
+            ),
+            requires_grad=False,
+        )
         layer.register_parameter("w2_weight", w2_weight)
         set_weight_attrs(w2_weight, extra_weight_attrs)
 
-        w2_weight_scale = torch.nn.Parameter(torch.zeros(
-            num_experts,
-            hidden_size,
-            intermediate_size_per_partition_after_pad // mxfp4_block,
-            dtype=scale_dtype),
-                                             requires_grad=False)
+        w2_weight_scale = torch.nn.Parameter(
+            torch.zeros(
+                num_experts,
+                hidden_size,
+                intermediate_size_per_partition_after_pad // mxfp4_block,
+                dtype=scale_dtype,
+            ),
+            requires_grad=False,
+        )
         layer.register_parameter("w2_weight_scale", w2_weight_scale)
         set_weight_attrs(w2_weight_scale, extra_weight_attrs)
 
-        w2_bias = torch.nn.Parameter(torch.zeros(num_experts,
-                                                 hidden_size,
-                                                 dtype=torch.bfloat16),
-                                     requires_grad=False)
+        w2_bias = torch.nn.Parameter(
+            torch.zeros(
+                num_experts,
+                hidden_size,
+                dtype=torch.bfloat16,
+            ),
+            requires_grad=False,
+        )
         layer.register_parameter("w2_bias", w2_bias)
         set_weight_attrs(w2_bias, extra_weight_attrs)