diff --git a/vllm/transformers_utils/configs/eagle.py b/vllm/transformers_utils/configs/eagle.py
index 31e3172c61ebf..377523efefc30 100644
--- a/vllm/transformers_utils/configs/eagle.py
+++ b/vllm/transformers_utils/configs/eagle.py
@@ -70,7 +70,8 @@ class EAGLEConfig(PretrainedConfig):
 
         if self.model is not None:
             for k, v in self.model.to_dict().items():
-                setattr(self, k, v)
+                if not hasattr(self, k):
+                    setattr(self, k, v)
 
     @classmethod
     def from_pretrained(
diff --git a/vllm/v1/spec_decode/eagle.py b/vllm/v1/spec_decode/eagle.py
index 671b98544387a..460d645a1a6c7 100644
--- a/vllm/v1/spec_decode/eagle.py
+++ b/vllm/v1/spec_decode/eagle.py
@@ -9,7 +9,6 @@ from vllm.distributed.parallel_state import get_pp_group
 from vllm.forward_context import set_forward_context
 from vllm.logger import init_logger
 from vllm.model_executor.model_loader import get_model
-from vllm.model_executor.models import supports_multimodal
 from vllm.model_executor.models.llama_eagle3 import Eagle3LlamaForCausalLM
 from vllm.triton_utils import tl, triton
 from vllm.v1.attention.backends.flash_attn import FlashAttentionMetadata
@@ -311,10 +310,7 @@ class EagleProposer:
         if self.vllm_config.speculative_config.method != "eagle3" and \
                 hasattr(target_model, "lm_head"):
             logger.info("Loading EAGLE LM head weights from the target model.")
-            if supports_multimodal(target_model):
-                self.model.lm_head = target_model.get_language_model().lm_head
-            else:
-                self.model.lm_head = target_model.lm_head
+            self.model.lm_head = target_model.lm_head
 
     @torch.inference_mode()
     def dummy_run(