diff --git a/tests/models/multimodal/processing/test_transformers.py b/tests/models/multimodal/processing/test_transformers.py
new file mode 100644
index 000000000000..c7d1b5271ff7
--- /dev/null
+++ b/tests/models/multimodal/processing/test_transformers.py
@@ -0,0 +1,40 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import pytest
+
+from vllm.assets.image import ImageAsset
+from vllm.config import ModelConfig
+from vllm.multimodal import MULTIMODAL_REGISTRY
+
+
+# yapf: disable
+@pytest.mark.parametrize("model_id",
+                         ["llava-hf/llava-onevision-qwen2-0.5b-ov-hf"])
+def test_multimodal_processor(model_id):
+    model_config = ModelConfig(
+        model=model_id,
+        model_impl="transformers",
+    )
+
+    mm_processor = MULTIMODAL_REGISTRY.create_processor(model_config, )
+
+    image_pil = ImageAsset('cherry_blossom').pil_image
+    mm_data = {"image": image_pil}
+    str_prompt = "<|im_start|>user <image>\nWhat is the content of this image?<|im_end|><|im_start|>assistant\n" # noqa: E501
+    str_processed_inputs = mm_processor.apply(
+        prompt=str_prompt,
+        mm_data=mm_data,
+        hf_processor_mm_kwargs={},
+    )
+
+    ids_prompt = [
+        151644, 872, 220, 151646, 198, 3838, 374, 279, 2213, 315, 419, 2168,
+        30, 151645, 151644, 77091, 198
+    ]
+    ids_processed_inputs = mm_processor.apply(
+        prompt=ids_prompt,
+        mm_data=mm_data,
+        hf_processor_mm_kwargs={},
+    )
+
+    assert str_processed_inputs["prompt"] == ids_processed_inputs["prompt"]
diff --git a/vllm/model_executor/models/transformers.py b/vllm/model_executor/models/transformers.py
index 47cff29caab0..8086f1bebac3 100644
--- a/vllm/model_executor/models/transformers.py
+++ b/vllm/model_executor/models/transformers.py
@@ -325,6 +325,11 @@ def apply(
 
         mm_items = self._to_mm_items(mm_data)
         hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
+        if not isinstance(prompt, str):
+            # the prompt is the tokenized ids which is not supported
+            # by the hf_processor, which is why we would need to decode the ids
+            # into string
+            prompt = hf_processor.decode(prompt)
 
         (prompt_ids, processed_data,
          mm_token_type_ids) = self._apply_hf_processor_text_mm(