diff --git a/vllm/model_executor/models/midashenglm.py b/vllm/model_executor/models/midashenglm.py
index e314ae357ecd4..140800dd41c76 100644
--- a/vllm/model_executor/models/midashenglm.py
+++ b/vllm/model_executor/models/midashenglm.py
@@ -497,8 +497,11 @@ class MiDashengLMDummyInputsBuilder(
 
         hf_processor = self.info.get_hf_processor()
         audio_token = hf_processor.audio_token
+        audio_bos_token = hf_processor.audio_bos_token
+        audio_eos_token = hf_processor.audio_eos_token
 
-        return audio_token * num_audios
+        single_audio_text = f"{audio_bos_token}{audio_token}{audio_eos_token}"
+        return single_audio_text * num_audios
 
     def get_dummy_mm_data(
         self,
@@ -577,14 +580,7 @@ class MiDashengLMMultiModalProcessor(
         vocab = tokenizer.get_vocab()
 
         audio_token = getattr(processor, "audio_token", "<|AUDIO|>")
-        audio_bos_token = getattr(processor, "audio_bos_token",
-                                  "<|audio_bos|>")
-        audio_eos_token = getattr(processor, "audio_eos_token",
-                                  "<|audio_eos|>")
-
         audio_token_id = vocab[audio_token]
-        audio_bos_id = vocab[audio_bos_token]
-        audio_eos_id = vocab[audio_eos_token]
 
         out_mm_data = out_mm_kwargs.get_data()
         audio_length = out_mm_data.get("audio_length")
@@ -604,7 +600,7 @@ class MiDashengLMMultiModalProcessor(
             audio_tokens = [audio_token_id] * num_features
 
             return PromptUpdateDetails.select_token_id(
-                [audio_bos_id] + audio_tokens + [audio_eos_id],
+                audio_tokens,
                 embed_token_id=audio_token_id,
             )
 
@@ -670,8 +666,18 @@ class MiDashengLMModel(nn.Module, SupportsMultiModal, SupportsPP):
                              f"Got type: {type(mm_input)}")
         if isinstance(mm_input, torch.Tensor):
             return mm_input.reshape(-1, *mm_input.shape[2:])
-        else:
-            return torch.concat(mm_input)
+
+        if name == "input_values":
+            max_length = max(tensor.shape[1] for tensor in mm_input)
+            padded_mm_input = [
+                torch.nn.functional.pad(tensor,
+                                        (0, max_length - tensor.shape[1]))
+                if tensor.shape[1] < max_length else tensor
+                for tensor in mm_input
+            ]
+            return torch.concat(padded_mm_input)
+
+        return torch.concat(mm_input)
 
     def _parse_and_validate_audio_input(
             self, **kwargs: object) -> Optional[MiDashengLMAudioInputs]: