diff --git a/vllm/model_executor/models/internlm2.py b/vllm/model_executor/models/internlm2.py
index c00b9a0ee6..6b1712ede3 100644
--- a/vllm/model_executor/models/internlm2.py
+++ b/vllm/model_executor/models/internlm2.py
@@ -41,6 +41,7 @@ from vllm.sequence import IntermediateTensors
 from .interfaces import SupportsLoRA, SupportsPP
 from .interfaces_base import default_pooling_type
 from .utils import (
+    AutoWeightsLoader,
     StageMissingLayer,
     is_pp_missing_parameter,
     make_empty_intermediate_tensors_factory,
@@ -308,6 +309,42 @@ class InternLM2Model(nn.Module):
         hidden_states, _ = self.norm(hidden_states, residual)
         return hidden_states
 
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("gate_up_proj", "w1", 0),
+            ("gate_up_proj", "w3", 1),
+        ]
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
 
 class InternLM2ForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
     packed_modules_mapping = {
@@ -368,40 +405,11 @@ class InternLM2ForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
         return logits
 
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
-        stacked_params_mapping = [
-            # (param_name, shard_name, shard_id)
-            ("gate_up_proj", "w1", 0),
-            ("gate_up_proj", "w3", 1),
-        ]
-        params_dict = dict(self.named_parameters())
-        loaded_params: set[str] = set()
-        for name, loaded_weight in weights:
-            if "rotary_emb.inv_freq" in name:
-                continue
-            for param_name, weight_name, shard_id in stacked_params_mapping:
-                if weight_name not in name:
-                    continue
-                name = name.replace(weight_name, param_name)
-                # Skip loading extra bias for GPTQ models.
-                if name.endswith(".bias") and name not in params_dict:
-                    continue
-                if is_pp_missing_parameter(name, self):
-                    continue
-                param = params_dict[name]
-                weight_loader = param.weight_loader
-                weight_loader(param, loaded_weight, shard_id)
-                break
-            else:
-                # Skip loading extra bias for GPTQ models.
-                if name.endswith(".bias") and name not in params_dict:
-                    continue
-                if is_pp_missing_parameter(name, self):
-                    continue
-                param = params_dict[name]
-                weight_loader = getattr(param, "weight_loader", default_weight_loader)
-                weight_loader(param, loaded_weight)
-            loaded_params.add(name)
-        return loaded_params
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=(["output."] if self.config.tie_word_embeddings else None),
+        )
+        return loader.load_weights(weights)
 
 
 @default_pooling_type(tok_pooling_type="ALL")