diff --git a/vllm/lora/models.py b/vllm/lora/models.py
index f7b35802f55b8..bfb65bbab9015 100644
--- a/vllm/lora/models.py
+++ b/vllm/lora/models.py
@@ -336,7 +336,7 @@ class LoRAModelManager:
         max_num_batched_tokens: int,
         vocab_size: int,
         lora_config: LoRAConfig,
-        model_config: Optional[ModelConfig],
+        model_config: ModelConfig | None,
         device: torch.device,
     ):
         """Create a LoRAModelManager and adapter for a given model.
@@ -709,7 +709,7 @@ class LoRAModelManager:
                 return any([module_name.startswith(prefix) for prefix in prefix_lst])
         return False
 
-    def _get_mm_punica_wrapper(self, module_name: str) -> Optional[PunicaWrapperBase]:
+    def _get_mm_punica_wrapper(self, module_name: str) -> PunicaWrapperBase | None:
         """
         Match the corresponding punica_wrapper based on module_name,
         and return None if lora is not supported for this module.
diff --git a/vllm/lora/worker_manager.py b/vllm/lora/worker_manager.py
index 49d4c29113760..dc8200fa8d006 100644
--- a/vllm/lora/worker_manager.py
+++ b/vllm/lora/worker_manager.py
@@ -71,7 +71,7 @@ class WorkerLoRAManager:
     def create_lora_manager(
         self,
         model: torch.nn.Module,
-        model_config: Optional[ModelConfig] = None,
+        model_config: ModelConfig | None = None,
     ) -> Any:
         lora_manager = create_lora_manager(
             model,
@@ -222,7 +222,7 @@ class LRUCacheWorkerLoRAManager(WorkerLoRAManager):
     def create_lora_manager(
         self,
         model: torch.nn.Module,
-        model_config: Optional[ModelConfig] = None,
+        model_config: ModelConfig | None = None,
     ) -> Any:
         lora_manager = create_lora_manager(
             model,
diff --git a/vllm/v1/worker/lora_model_runner_mixin.py b/vllm/v1/worker/lora_model_runner_mixin.py
index 539f413d34a9b..31094dcbda124 100644
--- a/vllm/v1/worker/lora_model_runner_mixin.py
+++ b/vllm/v1/worker/lora_model_runner_mixin.py
@@ -131,7 +131,10 @@ class LoRAModelRunnerMixin:
 
     @contextmanager
     def maybe_select_dummy_loras(
-        self, lora_config: LoRAConfig | None, num_scheduled_tokens: np.ndarray, is_mm_input: bool = False
+        self,
+        lora_config: LoRAConfig | None,
+        num_scheduled_tokens: np.ndarray,
+        is_mm_input: bool = False,
     ):
         if lora_config is None:
             yield