diff --git a/vllm/lora/utils.py b/vllm/lora/utils.py
index d72b7638d84af..4e9649f02856a 100644
--- a/vllm/lora/utils.py
+++ b/vllm/lora/utils.py
@@ -23,7 +23,7 @@
                               LogitsProcessorWithLoRA,
                               MergedColumnParallelLinearWithLoRA,
                               MergedQKVParallelLinearWithLora,
-                              QKVParallelLinearWithLora,
+                              ModulesToSaveWrapper, QKVParallelLinearWithLora,
                               ReplicatedLinearWithLoRA,
                               RowParallelLinearWithLoRA,
                               VocabParallelEmbeddingWithLoRA)
@@ -49,6 +49,7 @@
     MergedQKVParallelLinearWithShardedLora,
     RowParallelLinearWithShardedLoRA,
     LinearScalingRotaryEmbeddingWithLora,
+    ModulesToSaveWrapper,
 }
 
 
@@ -94,6 +95,7 @@ def replace_submodule(model: nn.Module, module_name: str,
 
 def parse_fine_tuned_lora_name(
         name: str,
+        enable_lora_modules_to_save: bool = False,
         weights_mapper: Optional[WeightsMapper] = None
 ) -> Tuple[str, bool, bool]:
     """Parse the name of lora weights.
@@ -106,7 +108,8 @@ def parse_fine_tuned_lora_name(
     return:
         Tuple(module_name, is_lora_a):
             module_name: the name of the module, e.g. model.dense1,
-            is_lora_a whether the tensor is lora_a or lora_b.
+            is_lora_a whether the tensor is lora_a or lora_b,
+              lora_a=None if this is module_to_save lm_head or token_embeds
             is_bias whether the tensor is lora bias.
     """
 
@@ -120,11 +123,22 @@ def parse_fine_tuned_lora_name(
         name = "base_model.model." + name
 
     parts = name.split(".")
-    if parts[-1] == "weight" and (parts[-2] == "lora_A"
-                                  or parts[-2] == "lora_B"):
-        new_name = ".".join(parts[2:-2])
-        return new_name, parts[-2] == "lora_A", False
 
+    
+    if parts[-1] == "weight":
+        if parts[-2] == "lora_A" or parts[-2] == "lora_B":
+            return ".".join(parts[2:-2]), parts[-2] == "lora_A", False
+        
+        if parts[-2] in ModulesToSaveWrapper.implemented_layers:
+
+            if not enable_lora_modules_to_save:
+                error_msg = f"""enable_lora_modules_to_save is False, 
+                but found tensor name {name} in LoRA checkpoint. 
+                Set enable_lora_modules_to_save=True to process
+                lm_head and embed_tokens as fully trained tensors"""
+                raise ValueError(error_msg)
+
+            return '.'.join(parts[2:-1]), None, False
     if parts[-1] == "lora_embedding_A" or parts[-1] == "lora_embedding_B":
         new_name = ".".join(parts[2:-1])
         return new_name, parts[-1] == "lora_embedding_A", False
diff --git a/vllm/lora/worker_manager.py b/vllm/lora/worker_manager.py
index 10976fac23028..9047694934831 100644
--- a/vllm/lora/worker_manager.py
+++ b/vllm/lora/worker_manager.py
@@ -91,8 +91,8 @@ def _load_adapter(self, lora_request: LoRARequest) -> LoRAModel:
                         packed_modules_mapping[module])
                 else:
                     expected_lora_modules.append(module)
-
             expected_lora_modules = list(set(expected_lora_modules))
+            expected_modules_to_save: List[str] = model.modules_to_save
             lora_path = get_adapter_absolute_path(lora_request.lora_path)
 
             # For some models like Qwen2VL, we need to use hf_to_vllm_mapper
@@ -105,9 +105,12 @@ def _load_adapter(self, lora_request: LoRARequest) -> LoRAModel:
             lora = self._lora_model_cls.from_local_checkpoint(
                 lora_path,
                 expected_lora_modules,
+                expected_modules_to_save,
                 max_position_embeddings=self.max_position_embeddings,
                 lora_model_id=lora_request.lora_int_id,
                 device="cpu",
+                enable_lora_modules_to_save=self._adapter_manager.lora_config.
+                enable_lora_modules_to_save,
                 dtype=self.lora_config.lora_dtype,
                 target_embedding_padding=self.vocab_size +
                 self.lora_config.lora_extra_vocab_size,