diff --git a/tests/lora/test_utils.py b/tests/lora/test_utils.py
index 892f6081e2aaa..4ff9715b4ca8d 100644
--- a/tests/lora/test_utils.py
+++ b/tests/lora/test_utils.py
@@ -1,12 +1,13 @@
 from collections import OrderedDict
 
+import pytest
 from torch import nn
 
 from vllm.lora.utils import parse_fine_tuned_lora_name, replace_submodule
 from vllm.utils import LRUCache
 
 
-def test_parse_fine_tuned_lora_name():
+def test_parse_fine_tuned_lora_name_valid():
     fixture = {
         ("base_model.model.lm_head.lora_A.weight", "lm_head", True),
         ("base_model.model.lm_head.lora_B.weight", "lm_head", False),
@@ -35,6 +36,17 @@ def test_parse_fine_tuned_lora_name():
         assert (module_name, is_lora_a) == parse_fine_tuned_lora_name(name)
 
 
+def test_parse_fine_tuned_lora_name_invalid():
+    fixture = {
+        "weight",
+        "base_model.weight",
+        "base_model.model.weight",
+    }
+    for name in fixture:
+        with pytest.raises(ValueError, match="unsupported LoRA weight"):
+            parse_fine_tuned_lora_name(name)
+
+
 def test_replace_submodule():
     model = nn.Sequential(
         OrderedDict([
diff --git a/vllm/lora/utils.py b/vllm/lora/utils.py
index b0198a50b1c52..4a86c16cf64db 100644
--- a/vllm/lora/utils.py
+++ b/vllm/lora/utils.py
@@ -94,13 +94,12 @@ def parse_fine_tuned_lora_name(name: str) -> Tuple[str, bool]:
             is_lora_a whether the tensor is lora_a or lora_b.
     """
     parts = name.split(".")
-    assert parts[0] == "base_model"
-    assert parts[1] == "model"
-    if parts[-1] == "weight":
-        assert parts[-2] == "lora_A" or parts[-2] == "lora_B"
-        return ".".join(parts[2:-2]), parts[-2] == "lora_A"
 
-    if parts[-1] == "lora_embedding_A" or parts[-1] == "lora_embedding_B":
-        return ".".join(parts[2:-1]), parts[-1] == "lora_embedding_A"
+    if len(parts) >= 2 and parts[0] == "base_model" and parts[1] == "model":
+        if parts[-1] == "weight":
+            if parts[-2] == "lora_A" or parts[-2] == "lora_B":
+                return ".".join(parts[2:-2]), parts[-2] == "lora_A"
+        elif parts[-1] == "lora_embedding_A" or parts[-1] == "lora_embedding_B":
+            return ".".join(parts[2:-1]), parts[-1] == "lora_embedding_A"
 
-    raise ValueError(f"{name} is unsupported format")
+    raise ValueError(f"{name} is unsupported LoRA weight")