diff --git a/vllm_ascend/worker/worker.py b/vllm_ascend/worker/worker.py
index 303dae36..2937aa4a 100644
--- a/vllm_ascend/worker/worker.py
+++ b/vllm_ascend/worker/worker.py
@@ -170,23 +170,25 @@ class NPUWorker(WorkerBase):
 
         hidden_size = self.vllm_config.model_config.hf_config.hidden_size
         model = self.model_runner.model
-        for name, param in model.named_parameters():
-            if 'w2_weight' in name and param.shape[2] == hidden_size:
-                parts = name.split('.')
-                param_name = parts[-1]
-                parent_module = model.get_submodule(".".join(parts[:-1]))
+        if tags is None or "weights" in tags:
+            for name, param in model.named_parameters():
+                if 'w2_weight' in name and param.shape[2] == hidden_size:
+                    parts = name.split('.')
+                    param_name = parts[-1]
+                    parent_module = model.get_submodule(".".join(parts[:-1]))
 
-                w2_data = param.transpose(1, 2)
-                w2_data = torch.nn.Parameter(w2_data, requires_grad=False)
-                setattr(parent_module, param_name, w2_data)
-            elif 'w13_weight' in name and param.shape[1] == hidden_size:
-                parts = name.split('.')
-                param_name = parts[-1]
-                parent_module = model.get_submodule(".".join(parts[:-1]))
+                    w2_data = param.transpose(1, 2)
+                    w2_data = torch.nn.Parameter(w2_data, requires_grad=False)
+                    setattr(parent_module, param_name, w2_data)
+                elif 'w13_weight' in name and param.shape[1] == hidden_size:
+                    parts = name.split('.')
+                    param_name = parts[-1]
+                    parent_module = model.get_submodule(".".join(parts[:-1]))
 
-                w13_data = param.transpose(1, 2)
-                w13_data = torch.nn.Parameter(w13_data, requires_grad=False)
-                setattr(parent_module, param_name, w13_data)
+                    w13_data = param.transpose(1, 2)
+                    w13_data = torch.nn.Parameter(w13_data,
+                                                  requires_grad=False)
+                    setattr(parent_module, param_name, w13_data)
 
         # Restore the buffers after level 2 sleep
         if len(self._sleep_saved_buffers):