diff --git a/docs/features/multimodal_inputs.md b/docs/features/multimodal_inputs.md
index 5dc1ebbf5bbe..4e54056d1b07 100644
--- a/docs/features/multimodal_inputs.md
+++ b/docs/features/multimodal_inputs.md
@@ -166,49 +166,51 @@ Full example: [examples/offline_inference/vision_language_multi_image.py](../../
 
 If using the [LLM.chat](../models/generative_models.md#llmchat) method, you can pass images directly in the message content using various formats: image URLs, PIL Image objects, or pre-computed embeddings:
 
-```python
-from vllm import LLM
-from vllm.assets.image import ImageAsset
-
-llm = LLM(model="llava-hf/llava-1.5-7b-hf")
-image_url = "https://picsum.photos/id/32/512/512"
-image_pil = ImageAsset('cherry_blossom').pil_image
-image_embeds = torch.load(...)
-
-conversation = [
-    {"role": "system", "content": "You are a helpful assistant"},
-    {"role": "user", "content": "Hello"},
-    {"role": "assistant", "content": "Hello! How can I assist you today?"},
-    {
-        "role": "user",
-        "content": [
-            {
-                "type": "image_url",
-                "image_url": {"url": image_url},
-            },
-            {
-                "type": "image_pil",
-                "image_pil": image_pil,
-            },
-            {
-                "type": "image_embeds",
-                "image_embeds": image_embeds,
-            },
-            {
-                "type": "text",
-                "text": "What's in these images?",
-            },
-        ],
-    },
-]
+??? code
 
-# Perform inference and log output.
-outputs = llm.chat(conversation)
+    ```python
+    from vllm import LLM
+    from vllm.assets.image import ImageAsset
 
-for o in outputs:
-    generated_text = o.outputs[0].text
-    print(generated_text)
-```
+    llm = LLM(model="llava-hf/llava-1.5-7b-hf")
+    image_url = "https://picsum.photos/id/32/512/512"
+    image_pil = ImageAsset('cherry_blossom').pil_image
+    image_embeds = torch.load(...)
+
+    conversation = [
+        {"role": "system", "content": "You are a helpful assistant"},
+        {"role": "user", "content": "Hello"},
+        {"role": "assistant", "content": "Hello! How can I assist you today?"},
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "image_url",
+                    "image_url": {"url": image_url},
+                },
+                {
+                    "type": "image_pil",
+                    "image_pil": image_pil,
+                },
+                {
+                    "type": "image_embeds",
+                    "image_embeds": image_embeds,
+                },
+                {
+                    "type": "text",
+                    "text": "What's in these images?",
+                },
+            ],
+        },
+    ]
+
+    # Perform inference and log output.
+    outputs = llm.chat(conversation)
+
+    for o in outputs:
+        generated_text = o.outputs[0].text
+        print(generated_text)
+    ```
 
 Multi-image input can be extended to perform video captioning. We show this with [Qwen2-VL](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct) as it supports videos:
 
@@ -893,6 +895,8 @@ The following example demonstrates how to pass image embeddings to the OpenAI se
 
 For Online Serving, you can also skip sending media if you expect cache hits with provided UUIDs. You can do so by sending media like this:
 
+??? code
+
     ```python
         # Image/video/audio URL:
         {