diff --git a/vllm_ascend/spec_decode/eagle_proposer.py b/vllm_ascend/spec_decode/eagle_proposer.py
index 74e2917806b..8d46338ae45 100644
--- a/vllm_ascend/spec_decode/eagle_proposer.py
+++ b/vllm_ascend/spec_decode/eagle_proposer.py
@@ -111,7 +111,24 @@ def load_model(self, model: nn.Module) -> None:
                 "The EAGLE head shares the same vocab embedding" \
                 " with the target model."
             )
-            self.model.model.embed_tokens = model.model.embed_tokens
+            if hasattr(model.model, "embed_tokens"):
+                target_embed_tokens = model.model.embed_tokens
+            elif hasattr(model.model, "embedding"):
+                target_embed_tokens = model.model.embedding
+            else:
+                raise AttributeError(
+                    "Target model does not have 'embed_tokens' or 'embedding' attribute"
+                )
+            # share embed_tokens with the target model if needed
+            if not self.model.has_own_embed_tokens:
+                logger.info("Draft model embed_tokens are uninitialized. "
+                            "Sharing vocab embedding with the target model.")
+                del self.model.model.embed_tokens
+                self.model.model.embed_tokens = target_embed_tokens
+            else:
+                logger.info(
+                    "Draft model embed_tokens are already initialized. "
+                    "Keeping separate vocab embedding from the target model.")
         else:
             logger.info(
                 "Since PP > 1, the EAGLE head loaded its own vocab embedding" \
@@ -121,12 +138,24 @@ def load_model(self, model: nn.Module) -> None:
         # share lm_head with the target model if needed
         # some model definition do not define lm_head explicitly
         # and reuse embed_tokens for lm_head, e.g., CohereForCausalLM
-        if self.name == SpecDcodeType.EAGLE and hasattr(model, "lm_head"):
-            logger.info("Loading EAGLE LM head weights from the target model.")
-            if supports_multimodal(model):
-                self.model.lm_head = model.get_language_model().lm_head
-            else:
+        if self.name == SpecDcodeType.EAGLE:
+            if hasattr(model, "lm_head"):
+                logger.info(
+                    "Loading EAGLE LM head weights from the target model.")
+                if supports_multimodal(model):
+                    self.model.lm_head = model.get_language_model().lm_head
+                else:
+                    self.model.lm_head = model.lm_head
+        else:
+            if (hasattr(model, "lm_head") and hasattr(self.model, "lm_head")
+                    and not self.model.has_own_lm_head):
+                logger.info("Draft model lm_head is uninitialized. "
+                            "Sharing lm_head with the target model.")
+                del self.model.lm_head
                 self.model.lm_head = model.lm_head
+            else:
+                logger.info("Draft model lm_head is already initialized. "
+                            "Keeping separate lm_head from the target model.")
 
     @torch.inference_mode()
     def dummy_run(self,