diff --git a/docs/contributing/model/multimodal.md b/docs/contributing/model/multimodal.md
index 2ec83fa27..c876cc47c 100644
--- a/docs/contributing/model/multimodal.md
+++ b/docs/contributing/model/multimodal.md
@@ -43,28 +43,73 @@ Further update the model as follows:
                 )
     ```
 
-- Implement [embed_multimodal][vllm.model_executor.models.interfaces.SupportsMultiModal.embed_multimodal] that returns the embeddings from running the multimodal inputs through the multimodal tokenizer of the model. Below we provide a boilerplate of a typical implementation pattern, but feel free to adjust it to your own needs.
+- Remove the embedding part from the [forward][torch.nn.Module.forward] method:
+    - Move the multi-modal embedding to [embed_multimodal][vllm.model_executor.models.interfaces.SupportsMultiModal.embed_multimodal].
+    - The text embedding and embedding merge are handled automatically by a default implementation of [embed_input_ids][vllm.model_executor.models.interfaces.SupportsMultiModal.embed_input_ids]. It does not need to be overridden in most cases.
 
-    ??? code
+    ```diff
+      def forward(
+          self,
+          input_ids: torch.Tensor | None,
+    -     pixel_values: torch.Tensor,
+          positions: torch.Tensor,
+          intermediate_tensors: IntermediateTensors | None = None,
+          inputs_embeds: torch.Tensor | None = None,
+      ) -> torch.Tensor:
+    -     if inputs_embeds is None:
+    -         inputs_embeds = self.get_input_embeddings()(input_ids)
+    -
+    -     if pixel_values is not None:
+    -         image_features = self.get_image_features(
+    -             pixel_values=pixel_values,
+    -         )
+    -         special_image_mask = self.get_placeholder_mask(
+    -             input_ids,
+    -             inputs_embeds=inputs_embeds,
+    -             image_features=image_features,
+    -         )
+    -         inputs_embeds = inputs_embeds.masked_scatter(
+    -             special_image_mask,
+    -             image_features,
+    -         )
 
-        ```python
-            def _process_image_input(self, image_input: YourModelImageInputs) -> torch.Tensor:
-                image_features = self.vision_encoder(image_input)
-                return self.multi_modal_projector(image_features)
+           hidden_states = self.language_model(
+               input_ids,
+               positions,
+               intermediate_tensors,
+               inputs_embeds=inputs_embeds,
+           )
+         ...
+  
+    +  def embed_multimodal(
+    +      self,
+    +      pixel_values: torch.Tensor,
+    +  ) -> MultiModalEmbeddings | None:
+    +      return self.get_image_features(
+    +          pixel_values=pixel_values,
+    +      )
+    ```
 
-            def embed_multimodal(
-                self,
-                **kwargs: object,
-            ) -> MultiModalEmbeddings | None:
-                # Validate the multimodal input keyword arguments
-                image_input = self._parse_and_validate_image_input(**kwargs)
-                if image_input is None:
-                    return None
+    Below we provide a boilerplate of a typical implementation pattern of [embed_multimodal][vllm.model_executor.models.interfaces.SupportsMultiModal.embed_multimodal], but feel free to adjust it to your own needs.
 
-                # Run multimodal inputs through encoder and projector
-                vision_embeddings = self._process_image_input(image_input)
-                return vision_embeddings
-        ```
+    ```python
+    def _process_image_input(self, image_input: YourModelImageInputs) -> torch.Tensor:
+        image_features = self.vision_encoder(image_input)
+        return self.multi_modal_projector(image_features)
+
+    def embed_multimodal(
+        self,
+        **kwargs: object,
+    ) -> MultiModalEmbeddings | None:
+        # Validate the multimodal input keyword arguments
+        image_input = self._parse_and_validate_image_input(**kwargs)
+        if image_input is None:
+            return None
+
+        # Run multimodal inputs through encoder and projector
+        vision_embeddings = self._process_image_input(image_input)
+        return vision_embeddings
+    ```
 
 !!! important
     The returned `multimodal_embeddings` must be either a **3D [torch.Tensor][]** of shape `(num_items, feature_size, hidden_size)`, or a **list / tuple of 2D [torch.Tensor][]'s** of shape `(feature_size, hidden_size)`, so that `multimodal_embeddings[i]` retrieves the embeddings generated from the `i`-th multimodal data item (e.g, image) of the request.