diff --git a/fastdeploy/model_executor/layers/linear.py b/fastdeploy/model_executor/layers/linear.py
index c6a62c935..d0a366e38 100644
--- a/fastdeploy/model_executor/layers/linear.py
+++ b/fastdeploy/model_executor/layers/linear.py
@@ -266,10 +266,6 @@ class ReplicatedLinear(LinearBase):
         )
 
         self.hidden_size = fd_config.model_config.hidden_size
-        self.weight_shape = [
-            self.input_size,
-            self.output_size,
-        ]
 
         assert self.quant_method is not None
         self.quant_method.create_weights(
@@ -311,24 +307,21 @@ class ColumnParallelLinear(LinearBase):
             add_bias (bool): Whether to add bias in the current layer or in the pre/post layer. Defaults to False.
             skip_quant (bool): Whether to skip quantization. Defaults to False.
         """
-        super().__init__(
-            fd_config=fd_config,
-            prefix=prefix,
-            input_size=input_size,
-            output_size=output_size,
-            with_bias=with_bias,
-            add_bias=add_bias,
-            skip_quant=skip_quant,
-        )
         self.fd_config = fd_config
         self.nranks = fd_config.parallel_config.tensor_parallel_size
         self.input_size = input_size
         self.output_size = divide(output_size, self.nranks)  # Split the output_size using TP inference.
         self.hidden_size = fd_config.model_config.hidden_size
-        self.weight_shape = [
-            self.input_size,
-            self.output_size,
-        ]
+
+        super().__init__(
+            fd_config=fd_config,
+            prefix=prefix,
+            input_size=self.input_size,
+            output_size=self.output_size,
+            with_bias=with_bias,
+            add_bias=add_bias,
+            skip_quant=skip_quant,
+        )
 
         assert self.quant_method is not None
         self.quant_method.create_weights(
@@ -634,15 +627,6 @@ class RowParallelLinear(LinearBase):
             add_bias (bool): Whether to add bias in the current layer or in the pre/post layer. Defaults to False.
             skip_quant (bool): Whether to skip quantization. Defaults to False.
         """
-        super().__init__(
-            fd_config=fd_config,
-            prefix=prefix,
-            input_size=input_size,
-            output_size=output_size,
-            with_bias=with_bias,
-            add_bias=add_bias,
-            skip_quant=skip_quant,
-        )
         self.fd_config = fd_config
         self.skip_quant = False
         self.nranks = fd_config.parallel_config.tensor_parallel_size
@@ -654,11 +638,15 @@ class RowParallelLinear(LinearBase):
         self.input_size = divide(input_size, self.nranks)
         self.output_size = output_size
 
-        self.weight_shape = [
-            self.input_size,
-            self.output_size,
-        ]
-        self._dtype = self._helper.get_default_dtype()
+        super().__init__(
+            fd_config=fd_config,
+            prefix=prefix,
+            input_size=self.input_size,
+            output_size=self.output_size,
+            with_bias=with_bias,
+            add_bias=add_bias,
+            skip_quant=skip_quant,
+        )
 
         assert self.quant_method is not None
         self.quant_method.create_weights(
diff --git a/fastdeploy/model_executor/models/qwen3.py b/fastdeploy/model_executor/models/qwen3.py
index 80c1897bd..04988740d 100644
--- a/fastdeploy/model_executor/models/qwen3.py
+++ b/fastdeploy/model_executor/models/qwen3.py
@@ -286,6 +286,9 @@ class Qwen3ForCausalLM(ModelForCasualLM):
                 weight_loader = getattr(param, "weight_loader", default_weight_loader(self.fd_config))
                 weight_loader(param, loaded_weight)
 
+        if self.tie_word_embeddings:
+            self.lm_head.linear.weight.set_value(self.model.embed_tokens.embeddings.weight.transpose([1, 0]))
+
     @paddle.no_grad()
     def set_state_dict(self, state_dict):
         """