diff --git a/tests/models/common/test_model_loader.py b/tests/models/common/test_model_loader.py
index c667e6ba4..dd1e1277e 100644
--- a/tests/models/common/test_model_loader.py
+++ b/tests/models/common/test_model_loader.py
@@ -201,6 +201,10 @@ def test_register_model_vllm_wrapper_methods():
     with pytest.raises(NotImplementedError, match="JAX model"):
         instance.forward(input_ids=None, positions=None)
 
+    # `get_input_embeddings` should be unimplemented.
+    with pytest.raises(NotImplementedError, match="JAX model"):
+        instance.get_input_embeddings(input_ids=None, positions=None)
+
     # `load_weights` should be a no-op that returns None.
     assert instance.load_weights() is None
 
diff --git a/tpu_inference/models/common/model_loader.py b/tpu_inference/models/common/model_loader.py
index 8c24ced9b..b33089715 100644
--- a/tpu_inference/models/common/model_loader.py
+++ b/tpu_inference/models/common/model_loader.py
@@ -421,6 +421,17 @@ def unimplemented_forward(
             "This is a JAX model and does not implement the PyTorch forward method."
         )
 
+    # Same as `forward`, this is a dummy method to satisfy vLLM's type checks.
+    def unimplemented_get_input_embeddings(
+        self,
+        input_ids: "torch.Tensor",
+        positions: "torch.Tensor",
+        inputs_embeds: Optional["torch.Tensor"] = None,
+    ) -> "torch.Tensor":
+        raise NotImplementedError(
+            "This is a JAX model and does not implement the PyTorch get_input_embeddings method."
+        )
+
     # We need a custom __init__ that only calls torch.nn.Module's init,
     # to avoid triggering JAX logic when vLLM inspects the class.
     def wrapper_init(self, *args, **kwargs):
@@ -434,6 +445,7 @@ def wrapper_init(self, *args, **kwargs):
         {
             "__init__": wrapper_init,
             "forward": unimplemented_forward,
+            "get_input_embeddings": unimplemented_get_input_embeddings,
             # Prevent vLLM from trying to load weights into this dummy class.
             "load_weights": lambda self, *args, **kwargs: None,
         })