multimodal_inputs.md

---
title: Multimodal Inputs
---
[](){ #multimodal-inputs }

This page teaches you how to pass multi-modal inputs to [multi-modal models][supported-mm-models] in vLLM.

!!! note
    We are actively iterating on multi-modal support. See [this RFC](gh-issue:4194) for upcoming changes,
    and [open an issue on GitHub](https://github.com/vllm-project/vllm/issues/new/choose) if you have any feedback or feature requests.

## Offline Inference

To input multi-modal data, follow this schema in [vllm.inputs.PromptType][]:

- `prompt`: The prompt should follow the format that is documented on HuggingFace.
- `multi_modal_data`: This is a dictionary that follows the schema defined in [vllm.multimodal.inputs.MultiModalDataDict][].

### Image Inputs

You can pass a single image to the `'image'` field of the multi-modal dictionary, as shown in the following examples:

??? Code

    ```python
    from vllm import LLM

    llm = LLM(model="llava-hf/llava-1.5-7b-hf")

    # Refer to the HuggingFace repo for the correct format to use
    prompt = "USER: <image>\nWhat is the content of this image?\nASSISTANT:"

    # Load the image using PIL.Image
    image = PIL.Image.open(...)

    # Single prompt inference
    outputs = llm.generate({
        "prompt": prompt,
        "multi_modal_data": {"image": image},
    })

    for o in outputs:
        generated_text = o.outputs[0].text
        print(generated_text)

    # Batch inference
    image_1 = PIL.Image.open(...)
    image_2 = PIL.Image.open(...)
    outputs = llm.generate(
        [
            {
                "prompt": "USER: <image>\nWhat is the content of this image?\nASSISTANT:",
                "multi_modal_data": {"image": image_1},
            },
            {
                "prompt": "USER: <image>\nWhat's the color of this image?\nASSISTANT:",
                "multi_modal_data": {"image": image_2},
            }
        ]
    )

    for o in outputs:
        generated_text = o.outputs[0].text
        print(generated_text)
    ```

Full example: <gh-file:examples/offline_inference/vision_language.py>

To substitute multiple images inside the same text prompt, you can pass in a list of images instead:

??? Code

    ```python
    from vllm import LLM

    llm = LLM(
        model="microsoft/Phi-3.5-vision-instruct",
        trust_remote_code=True,  # Required to load Phi-3.5-vision
        max_model_len=4096,  # Otherwise, it may not fit in smaller GPUs
        limit_mm_per_prompt={"image": 2},  # The maximum number to accept
    )

    # Refer to the HuggingFace repo for the correct format to use
    prompt = "<|user|>\n<|image_1|>\n<|image_2|>\nWhat is the content of each image?<|end|>\n<|assistant|>\n"

    # Load the images using PIL.Image
    image1 = PIL.Image.open(...)
    image2 = PIL.Image.open(...)

    outputs = llm.generate({
        "prompt": prompt,
        "multi_modal_data": {
            "image": [image1, image2]
        },
    })

    for o in outputs:
        generated_text = o.outputs[0].text
        print(generated_text)
    ```

Full example: <gh-file:examples/offline_inference/vision_language_multi_image.py>

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:

??? Code

    ```python
    from vllm import LLM

    # Specify the maximum number of frames per video to be 4. This can be changed.
    llm = LLM("Qwen/Qwen2-VL-2B-Instruct", limit_mm_per_prompt={"image": 4})

    # Create the request payload.
    video_frames = ... # load your video making sure it only has the number of frames specified earlier.
    message = {
        "role": "user",
        "content": [
            {"type": "text", "text": "Describe this set of frames. Consider the frames to be a part of the same video."},
        ],
    }
    for i in range(len(video_frames)):
        base64_image = encode_image(video_frames[i]) # base64 encoding.
        new_image = {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}}
        message["content"].append(new_image)

    # Perform inference and log output.
    outputs = llm.chat([message])

    for o in outputs:
        generated_text = o.outputs[0].text
        print(generated_text)
    ```

### Video Inputs

You can pass a list of NumPy arrays directly to the `'video'` field of the multi-modal dictionary
instead of using multi-image input.

Full example: <gh-file:examples/offline_inference/vision_language.py>

### Audio Inputs

You can pass a tuple `(array, sampling_rate)` to the `'audio'` field of the multi-modal dictionary.

Full example: <gh-file:examples/offline_inference/audio_language.py>

### Embedding Inputs

To input pre-computed embeddings belonging to a data type (i.e. image, video, or audio) directly to the language model,
pass a tensor of shape `(num_items, feature_size, hidden_size of LM)` to the corresponding field of the multi-modal dictionary.

??? Code

    ```python
    from vllm import LLM

    # Inference with image embeddings as input
    llm = LLM(model="llava-hf/llava-1.5-7b-hf")

    # Refer to the HuggingFace repo for the correct format to use
    prompt = "USER: <image>\nWhat is the content of this image?\nASSISTANT:"

    # Embeddings for single image
    # torch.Tensor of shape (1, image_feature_size, hidden_size of LM)
    image_embeds = torch.load(...)

    outputs = llm.generate({
        "prompt": prompt,
        "multi_modal_data": {"image": image_embeds},
    })

    for o in outputs:
        generated_text = o.outputs[0].text
        print(generated_text)
    ```

For Qwen2-VL and MiniCPM-V, we accept additional parameters alongside the embeddings:

??? Code

    ```python
    # Construct the prompt based on your model
    prompt = ...

    # Embeddings for multiple images
    # torch.Tensor of shape (num_images, image_feature_size, hidden_size of LM)
    image_embeds = torch.load(...)

    # Qwen2-VL
    llm = LLM("Qwen/Qwen2-VL-2B-Instruct", limit_mm_per_prompt={"image": 4})
    mm_data = {
        "image": {
            "image_embeds": image_embeds,
            # image_grid_thw is needed to calculate positional encoding.
            "image_grid_thw": torch.load(...),  # torch.Tensor of shape (1, 3),
        }
    }

    # MiniCPM-V
    llm = LLM("openbmb/MiniCPM-V-2_6", trust_remote_code=True, limit_mm_per_prompt={"image": 4})
    mm_data = {
        "image": {
            "image_embeds": image_embeds,
            # image_sizes is needed to calculate details of the sliced image.
            "image_sizes": [image.size for image in images],  # list of image sizes
        }
    }

    outputs = llm.generate({
        "prompt": prompt,
        "multi_modal_data": mm_data,
    })

    for o in outputs:
        generated_text = o.outputs[0].text
        print(generated_text)
    ```

## Online Serving

Our OpenAI-compatible server accepts multi-modal data via the [Chat Completions API](https://platform.openai.com/docs/api-reference/chat).

!!! important
    A chat template is **required** to use Chat Completions API.
    For HF format models, the default chat template is defined inside `chat_template.json` or `tokenizer_config.json`.

    If no default chat template is available, we will first look for a built-in fallback in <gh-file:vllm/transformers_utils/chat_templates/registry.py>.
    If no fallback is available, an error is raised and you have to provide the chat template manually via the `--chat-template` argument.

    For certain models, we provide alternative chat templates inside <gh-dir:examples>.
    For example, VLM2Vec uses <gh-file:examples/template_vlm2vec.jinja> which is different from the default one for Phi-3-Vision.

### Image Inputs

Image input is supported according to [OpenAI Vision API](https://platform.openai.com/docs/guides/vision).
Here is a simple example using Phi-3.5-Vision.

First, launch the OpenAI-compatible server:

```bash
vllm serve microsoft/Phi-3.5-vision-instruct --task generate \
  --trust-remote-code --max-model-len 4096 --limit-mm-per-prompt '{"image":2}'
```

Then, you can use the OpenAI client as follows:

??? Code

    ```python
    from openai import OpenAI

    openai_api_key = "EMPTY"
    openai_api_base = "http://localhost:8000/v1"

    client = OpenAI(
        api_key=openai_api_key,
        base_url=openai_api_base,
    )

    # Single-image input inference
    image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"

    chat_response = client.chat.completions.create(
        model="microsoft/Phi-3.5-vision-instruct",
        messages=[{
            "role": "user",
            "content": [
                # NOTE: The prompt formatting with the image token `<image>` is not needed
                # since the prompt will be processed automatically by the API server.
                {"type": "text", "text": "What’s in this image?"},
                {"type": "image_url", "image_url": {"url": image_url}},
            ],
        }],
    )
    print("Chat completion output:", chat_response.choices[0].message.content)

    # Multi-image input inference
    image_url_duck = "https://upload.wikimedia.org/wikipedia/commons/d/da/2015_Kaczka_krzy%C5%BCowka_w_wodzie_%28samiec%29.jpg"
    image_url_lion = "https://upload.wikimedia.org/wikipedia/commons/7/77/002_The_lion_king_Snyggve_in_the_Serengeti_National_Park_Photo_by_Giles_Laurent.jpg"

    chat_response = client.chat.completions.create(
        model="microsoft/Phi-3.5-vision-instruct",
        messages=[{
            "role": "user",
            "content": [
                {"type": "text", "text": "What are the animals in these images?"},
                {"type": "image_url", "image_url": {"url": image_url_duck}},
                {"type": "image_url", "image_url": {"url": image_url_lion}},
            ],
        }],
    )
    print("Chat completion output:", chat_response.choices[0].message.content)
    ```

Full example: <gh-file:examples/online_serving/openai_chat_completion_client_for_multimodal.py>

!!! tip
    Loading from local file paths is also supported on vLLM: You can specify the allowed local media path via `--allowed-local-media-path` when launching the API server/engine,
    and pass the file path as `url` in the API request.

!!! tip
    There is no need to place image placeholders in the text content of the API request - they are already represented by the image content.
    In fact, you can place image placeholders in the middle of the text by interleaving text and image content.

!!! note
    By default, the timeout for fetching images through HTTP URL is `5` seconds.
    You can override this by setting the environment variable:

    ```bash
    export VLLM_IMAGE_FETCH_TIMEOUT=<timeout>
    ```

### Video Inputs

Instead of `image_url`, you can pass a video file via `video_url`. Here is a simple example using [LLaVA-OneVision](https://huggingface.co/llava-hf/llava-onevision-qwen2-0.5b-ov-hf).

First, launch the OpenAI-compatible server:

```bash
vllm serve llava-hf/llava-onevision-qwen2-0.5b-ov-hf --task generate --max-model-len 8192
```

Then, you can use the OpenAI client as follows:

??? Code

    ```python
    from openai import OpenAI

    openai_api_key = "EMPTY"
    openai_api_base = "http://localhost:8000/v1"

    client = OpenAI(
        api_key=openai_api_key,
        base_url=openai_api_base,
    )

    video_url = "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/ForBiggerFun.mp4"

    ## Use video url in the payload
    chat_completion_from_url = client.chat.completions.create(
        messages=[{
            "role":
            "user",
            "content": [
                {
                    "type": "text",
                    "text": "What's in this video?"
                },
                {
                    "type": "video_url",
                    "video_url": {
                        "url": video_url
                    },
                },
            ],
        }],
        model=model,
        max_completion_tokens=64,
    )

    result = chat_completion_from_url.choices[0].message.content
    print("Chat completion output from image url:", result)
    ```

Full example: <gh-file:examples/online_serving/openai_chat_completion_client_for_multimodal.py>

!!! note
    By default, the timeout for fetching videos through HTTP URL is `30` seconds.
    You can override this by setting the environment variable:

    ```bash
    export VLLM_VIDEO_FETCH_TIMEOUT=<timeout>
    ```

### Audio Inputs

Audio input is supported according to [OpenAI Audio API](https://platform.openai.com/docs/guides/audio?audio-generation-quickstart-example=audio-in).
Here is a simple example using Ultravox-v0.5-1B.

First, launch the OpenAI-compatible server:

```bash
vllm serve fixie-ai/ultravox-v0_5-llama-3_2-1b
```

Then, you can use the OpenAI client as follows:

??? Code

    ```python
    import base64
    import requests
    from openai import OpenAI
    from vllm.assets.audio import AudioAsset

    def encode_base64_content_from_url(content_url: str) -> str:
        """Encode a content retrieved from a remote url to base64 format."""

        with requests.get(content_url) as response:
            response.raise_for_status()
            result = base64.b64encode(response.content).decode('utf-8')

        return result

    openai_api_key = "EMPTY"
    openai_api_base = "http://localhost:8000/v1"

    client = OpenAI(
        api_key=openai_api_key,
        base_url=openai_api_base,
    )

    # Any format supported by librosa is supported
    audio_url = AudioAsset("winning_call").url
    audio_base64 = encode_base64_content_from_url(audio_url)

    chat_completion_from_base64 = client.chat.completions.create(
        messages=[{
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "What's in this audio?"
                },
                {
                    "type": "input_audio",
                    "input_audio": {
                        "data": audio_base64,
                        "format": "wav"
                    },
                },
            ],
        }],
        model=model,
        max_completion_tokens=64,
    )

    result = chat_completion_from_base64.choices[0].message.content
    print("Chat completion output from input audio:", result)
    ```

Alternatively, you can pass `audio_url`, which is the audio counterpart of `image_url` for image input:

??? Code

    ```python
    chat_completion_from_url = client.chat.completions.create(
        messages=[{
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "What's in this audio?"
                },
                {
                    "type": "audio_url",
                    "audio_url": {
                        "url": audio_url
                    },
                },
            ],
        }],
        model=model,
        max_completion_tokens=64,
    )

    result = chat_completion_from_url.choices[0].message.content
    print("Chat completion output from audio url:", result)
    ```

Full example: <gh-file:examples/online_serving/openai_chat_completion_client_for_multimodal.py>

!!! note
    By default, the timeout for fetching audios through HTTP URL is `10` seconds.
    You can override this by setting the environment variable:

    ```bash
    export VLLM_AUDIO_FETCH_TIMEOUT=<timeout>
    ```

### Embedding Inputs

To input pre-computed embeddings belonging to a data type (i.e. image, video, or audio) directly to the language model,
pass a tensor of shape to the corresponding field of the multi-modal dictionary.
#### Image Embedding Inputs
For image embeddings, you can pass the base64-encoded tensor to the `image_embeds` field.
The following example demonstrates how to pass image embeddings to the OpenAI server:

??? Code

    ```python
    image_embedding = torch.load(...)
    grid_thw = torch.load(...) # Required by Qwen/Qwen2-VL-2B-Instruct

    buffer = io.BytesIO()
    torch.save(image_embedding, buffer)
    buffer.seek(0)
    binary_data = buffer.read()
    base64_image_embedding = base64.b64encode(binary_data).decode('utf-8')

    client = OpenAI(
        # defaults to os.environ.get("OPENAI_API_KEY")
        api_key=openai_api_key,
        base_url=openai_api_base,
    )

    # Basic usage - this is equivalent to the LLaVA example for offline inference
    model = "llava-hf/llava-1.5-7b-hf"
    embeds =  {
        "type": "image_embeds",
        "image_embeds": f"{base64_image_embedding}" 
    }

    # Pass additional parameters (available to Qwen2-VL and MiniCPM-V)
    model = "Qwen/Qwen2-VL-2B-Instruct"
    embeds =  {
        "type": "image_embeds",
        "image_embeds": {
            "image_embeds": f"{base64_image_embedding}" , # Required
            "image_grid_thw": f"{base64_image_grid_thw}"  # Required by Qwen/Qwen2-VL-2B-Instruct
        },
    }
    model = "openbmb/MiniCPM-V-2_6"
    embeds =  {
        "type": "image_embeds",
        "image_embeds": {
            "image_embeds": f"{base64_image_embedding}" , # Required
            "image_sizes": f"{base64_image_sizes}"  # Required by openbmb/MiniCPM-V-2_6
        },
    }
    chat_completion = client.chat.completions.create(
        messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": [
            {
                "type": "text",
                "text": "What's in this image?",
            },
            embeds,
            ],
        },
    ],
        model=model,
    )
    ```

!!! note
    Only one message can contain `{"type": "image_embeds"}`.
    If used with a model that requires additional parameters, you must also provide a tensor for each of them, e.g. `image_grid_thw`, `image_sizes`, etc.