diff --git a/tests/lora/test_default_mm_loras.py b/tests/lora/test_default_mm_loras.py
index 1d16862b30e5..c76d3c6e798e 100644
--- a/tests/lora/test_default_mm_loras.py
+++ b/tests/lora/test_default_mm_loras.py
@@ -153,5 +153,5 @@ class MockEngineException(Exception):
         # Then check to make sure the submitted lora request
         # and text prompt were zipped together correctly
         engine_args, engine_kwargs = mock_add_request.call_args
+        assert engine_args[1]["prompt"] == AUDIO_PROMPT
         assert engine_kwargs["lora_request"] is None
-        assert engine_kwargs["prompt_text"] == AUDIO_PROMPT
diff --git a/tests/lora/test_qwenvl.py b/tests/lora/test_qwenvl.py
index 273f587f07aa..741e1aceee63 100644
--- a/tests/lora/test_qwenvl.py
+++ b/tests/lora/test_qwenvl.py
@@ -88,9 +88,8 @@ def run_test(
         # Validate outputs
         for generated, expected in zip(generated_texts, expected_outputs):
             assert expected.startswith(generated), (
-                f"Generated text {generated} doesn't "
+                f"Generated text {generated} doesn't match expected pattern {expected}"
             )
-            f"match expected pattern {expected}"
 
     def run_beam_search_test(
         self,
@@ -118,11 +117,14 @@ def run_beam_search_test(
             inputs, beam_search_params, lora_request=lora_request
         )
 
-        for output_obj, expected_outs in zip(outputs, expected_outputs):
+        for output_obj, expected_texts in zip(outputs, expected_outputs):
             output_texts = [seq.text for seq in output_obj.sequences]
-            assert output_texts == expected_outs, (
-                f"Generated texts {output_texts} do not match expected {expected_outs}"
-            )  # noqa: E501
+
+            for output_text, expected_text in zip(output_texts, expected_texts):
+                # NOTE beam search .text contains the whole text including inputs
+                assert output_text.endswith(expected_text), (
+                    f"Generated {output_text} does not match expected {expected_text}"
+                )
 
 
 TEST_IMAGES = [
@@ -151,11 +153,10 @@ def run_beam_search_test(
     "A closeup shot of the Tokyo Skytree with pink flowers in the foreground.",
 ]
 
-# NOTE - beam search .text contains the whole text
 EXPECTED_BEAM_SEARCH_OUTPUTS = [
     [
-        "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>What is in the image?<|im_end|>\n<|im_start|>assistant\nA majestic skyscraper stands",  # noqa: E501
-        "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>What is in the image?<|im_end|>\n<|im_start|>assistant\nA majestic tower stands tall",  # noqa: E501
+        "A majestic skyscraper stands",
+        "A majestic tower stands tall",
     ],
 ]
 
diff --git a/vllm/entrypoints/llm.py b/vllm/entrypoints/llm.py
index 57fd4b67c135..9d1e2912c4b2 100644
--- a/vllm/entrypoints/llm.py
+++ b/vllm/entrypoints/llm.py
@@ -542,51 +542,31 @@ def wait_for_completion(
         outputs = self._run_engine(use_tqdm=use_tqdm)
         return self.engine_class.validate_outputs(outputs, RequestOutput)
 
-    def _resolve_lora_reqs(
-        self,
-        prompts: Sequence[ProcessorInputs],
-        lora_request: Sequence[LoRARequest | None] | LoRARequest | None,
-    ):
-        lora_config = self.llm_engine.vllm_config.lora_config
-        seq_lora_requests = self._lora_request_to_seq(lora_request, len(prompts))
-
-        if (
-            lora_config is None
-            or not self.model_config.is_multimodal_model
-            or (lora_config and lora_config.default_mm_loras is None)
-        ):
-            return seq_lora_requests
-
-        return [
-            self._resolve_single_prompt_mm_lora(
-                prompt,
-                lora_req,
-                lora_config.default_mm_loras,
-            )
-            for prompt, lora_req in zip(prompts, seq_lora_requests)
-        ]
-
-    def _resolve_single_prompt_mm_lora(
+    def _resolve_mm_lora(
         self,
         prompt: ProcessorInputs,
         lora_request: LoRARequest | None,
-        default_mm_loras: dict[str, str] | None,
-    ):
-        if not default_mm_loras or prompt["type"] != "multimodal":
+    ) -> LoRARequest | None:
+        if prompt["type"] != "multimodal":
+            return lora_request
+
+        lora_config = self.llm_engine.vllm_config.lora_config
+        default_mm_loras = None if lora_config is None else lora_config.default_mm_loras
+        if not default_mm_loras:
             return lora_request
 
         prompt_modalities = prompt["mm_placeholders"].keys()
         intersection = set(prompt_modalities).intersection(default_mm_loras.keys())
         if not intersection:
             return lora_request
+
         if len(intersection) > 1:
             # TODO: Would be nice to be able to have multiple loras per prompt
             logger.warning(
-                "Multiple modality specific loras were registered and would be"
-                " used by a single prompt consuming several modalities; "
-                " currently we only support one lora per request; as such,"
-                " lora(s) registered with modalities: %s"
-                " will be skipped",
+                "Multiple modality specific loras were registered and would be "
+                "used by a single prompt consuming several modalities; "
+                "currently we only support one lora per request; as such, "
+                "lora(s) registered with modalities: %s will be skipped",
                 intersection,
             )
             return lora_request
@@ -1915,7 +1895,10 @@ def _render_and_add_requests(
                 request_id = self._add_request(
                     prompt,
                     params[i],
-                    lora_request=None if lora_requests is None else lora_requests[i],
+                    lora_request=self._resolve_mm_lora(
+                        prompt,
+                        None if lora_requests is None else lora_requests[i],
+                    ),
                     priority=0 if priorities is None else priorities[i],
                 )
                 added_request_ids.append(request_id)