diff --git a/fastdeploy/model_executor/models/ernie4_5_moe.py b/fastdeploy/model_executor/models/ernie4_5_moe.py
index e771d497a5..c43bc801c7 100644
--- a/fastdeploy/model_executor/models/ernie4_5_moe.py
+++ b/fastdeploy/model_executor/models/ernie4_5_moe.py
@@ -625,7 +625,7 @@ class Ernie4_5_MoeForCausalLM(ModelForCasualLM):
             )
             process_weights_after_loading_fn(model_sublayer_name, param)
 
-        if self.tie_word_embeddings:
+        if getattr(self, "tie_word_embeddings", False):
             self.lm_head.load_state_dict({self.lm_head.weight_key: self.ernie.embed_tokens.embeddings.weight})
 
     def compute_logits(self, hidden_states: paddle.Tensor):
diff --git a/fastdeploy/model_executor/models/ernie4_5_mtp.py b/fastdeploy/model_executor/models/ernie4_5_mtp.py
index 7944f9f08d..48c2c9b2b3 100644
--- a/fastdeploy/model_executor/models/ernie4_5_mtp.py
+++ b/fastdeploy/model_executor/models/ernie4_5_mtp.py
@@ -16,7 +16,6 @@
 
 from __future__ import annotations
 
-import re
 from functools import partial
 from typing import Dict, Union
 
@@ -356,7 +355,6 @@ class Ernie4_5_MTPForCausalLM(ModelForCasualLM):
         self.ori_vocab_size = fd_config.model_config.ori_vocab_size
 
         self.lm_head = fd_config.speculative_config.sharing_model.lm_head
-        self.tie_word_embeddings = fd_config.model_config.tie_word_embeddings
 
     @classmethod
     def name(self):
@@ -374,11 +372,6 @@ class Ernie4_5_MTPForCausalLM(ModelForCasualLM):
                 and values are NumPy arrays or PaddlePaddle tensors.
         """
         self.ernie.load_state_dict(state_dict)
-        # if self.tie_word_embeddings:
-        #     self.lm_head.linear.weight.set_value(
-        #         self.ernie.embed_tokens.embeddings.weight.transpose([1, 0]))
-        # else:
-        #     self.lm_head.load_state_dict(state_dict)
 
     @paddle.no_grad()
     def load_weights(self, weights_iterator) -> None:
@@ -388,45 +381,22 @@ class Ernie4_5_MTPForCausalLM(ModelForCasualLM):
         Args:
             weights_iterator (Iterator): An iterator yielding (name, weight) pairs.
         """
-
-        from fastdeploy.model_executor.utils import (
-            default_weight_loader,
-            process_weights_after_loading,
+        from fastdeploy.model_executor.models.ernie4_5_moe import (
+            Ernie4_5_MoeForCausalLM,
         )
+        from fastdeploy.model_executor.utils import remap_weight_keys
 
-        all_param_mapping = [
-            # (param_name, weight_name, expert_id, shard_id)
-            ("embed_tokens.embeddings", "embed_tokens", None, None),
-            ("lm_head.linear", "lm_head", None, None),
-            ("enorm", "mtp_emb_norm.0", None, None),
-            ("hnorm", "mtp_hidden_norm.0", None, None),
-            ("eh_proj.linear", "mtp_linear_proj.0", None, None),
-        ]
-
-        params_dict = dict(self.named_parameters())
-        shard_id = None
-        process_weights_after_loading_fn = process_weights_after_loading(dict(self.named_sublayers()))
-        for loaded_weight_name, loaded_weight in weights_iterator:
-            for param_name, weight_name, exp_id, shard_id in all_param_mapping:
-                if weight_name not in loaded_weight_name:
-                    continue
-                model_param_name = loaded_weight_name.replace(weight_name, param_name)
-                param = params_dict[model_param_name]
-                shard_id = shard_id
-                break
-            else:
-                if loaded_weight_name not in params_dict.keys():
-                    continue
-                model_param_name = loaded_weight_name
-                param = params_dict[loaded_weight_name]
-
-            # Get weight loader from parameter and set weight
-            weight_loader = getattr(param, "weight_loader", default_weight_loader(self.fd_config))
-            weight_loader(param, loaded_weight)
-            model_sublayer_name = re.sub(
-                r"\.(up_gate_proj_weight|down_proj_weight|weight|cache_k_scale|cache_v_scale)$", "", model_param_name
-            )
-            process_weights_after_loading_fn(model_sublayer_name, param)
+        Ernie4_5_MoeForCausalLM.load_weights(
+            self,
+            remap_weight_keys(
+                weights_iterator,
+                {
+                    "mtp_emb_norm.0": "enorm",
+                    "mtp_hidden_norm.0": "hnorm",
+                    "mtp_linear_proj.0": "eh_proj.linear",
+                },
+            ),
+        )
 
     def compute_logits(self, hidden_states: paddle.Tensor):
         """
diff --git a/fastdeploy/model_executor/models/ernie4_5_vl/ernie4_5_vl_moe.py b/fastdeploy/model_executor/models/ernie4_5_vl/ernie4_5_vl_moe.py
index 5b8e4e13db..5097cee9ed 100644
--- a/fastdeploy/model_executor/models/ernie4_5_vl/ernie4_5_vl_moe.py
+++ b/fastdeploy/model_executor/models/ernie4_5_vl/ernie4_5_vl_moe.py
@@ -724,7 +724,7 @@ class Ernie4_5_VLMoeForConditionalGeneration(ModelForCasualLM):
                 r"\.(up_gate_proj_weight|down_proj_weight|weight|cache_k_scale|cache_v_scale)$", "", model_param_name
             )
             process_weights_after_loading_fn(model_sublayer_name, param)
-        if self.tie_word_embeddings:
+        if getattr(self, "tie_word_embeddings", False):
             # because we use lazy guard and is not initialized by default
             if not self.lm_head.linear.weight._is_initialized():
                 self.lm_head.linear.weight.initialize()
diff --git a/fastdeploy/model_executor/utils.py b/fastdeploy/model_executor/utils.py
index 28278d5654..4b7dff4bea 100644
--- a/fastdeploy/model_executor/utils.py
+++ b/fastdeploy/model_executor/utils.py
@@ -166,6 +166,13 @@ class WeightsMapper:
         return self._map_name(weight_name)
 
 
+def remap_weight_keys(weights_iterator, mapper: dict):
+    return (
+        (next((key.replace(k, v) for k, v in mapper.items() if k in key), key), value)
+        for key, value in weights_iterator
+    )
+
+
 def process_weights_before_loading(
     *, skip_prefixes: Optional[List[str]] = None, mapper: Optional[WeightsMapper] = None
 ):