[Bugfix] Fix broken Minimax-01-VL model

examples/offline_inference/vision_language.py

@@ -766,6 +766,40 @@ def run_minicpmv(questions: list[str], modality: str) -> ModelRequestData:
     return run_minicpmv_base(questions, modality, "openbmb/MiniCPM-V-2_6")
 
 
+def run_minimax(questions: list[str], modality: str) -> ModelRequestData:
+    assert modality == "image"
+
+    model_name = "MiniMaxAI/MiniMax-VL-01"
+
+    # The configuration below has been confirmed to launch on a single L40 GPU.
+    engine_args = EngineArgs(
+        model=model_name,
+        max_model_len=14336,
+        max_num_seqs=2,
+        limit_mm_per_prompt={modality: 1},
+        trust_remote_code=True,
+    )
+
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    messages = [
+        [
+            {
+                "role": "user",
+                "content": [{"type": "image"}, {"type": "text", "text": question}],
+            }
+        ]
+        for question in questions
+    ]
+    prompts = tokenizer.apply_chat_template(
+        messages, add_generation_prompt=True, tokenize=False
+    )
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompts=prompts,
+    )
+
+
 # Mistral-3 HF-format
 def run_mistral3(questions: list[str], modality: str) -> ModelRequestData:
     assert modality == "image"
@@ -1412,6 +1446,7 @@ def run_tarsier2(questions: list[str], modality: str) -> ModelRequestData:
     "mantis": run_mantis,
     "minicpmo": run_minicpmo,
     "minicpmv": run_minicpmv,
+    "minimax": run_minimax,
     "mistral3": run_mistral3,
     "mllama": run_mllama,
     "molmo": run_molmo,

vllm/model_executor/models/minimax_vl_01.py

@@ -33,7 +33,8 @@ class MiniMaxVL01ImagePixelInputs(TypedDict):
     type: Literal["pixel_values"]
     pixel_values: torch.Tensor
     """
-    Shape: `(batch_size * num_images, num_channels, height, width)`
+    Shape: 
+    `(batch_size * num_images * num_patches, num_channels, height, width)`
 
     Note that `height` or `width` may be different per batch and image,
     in which case the data is passed as a list instead of a batched tensor.
@@ -312,7 +313,7 @@ def _parse_and_validate_image_input(
             return MiniMaxVL01ImagePixelInputs(
                 type="pixel_values",
                 pixel_values=self._validate_pixel_values(
-                    flatten_bn(pixel_values, concat=True)),
+                    flatten_bn(flatten_bn(pixel_values), concat=True)),
             )
 
         if image_embeds is not None: