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*This model was released on 2025-03-03 and added to Hugging Face Transformers on 2025-03-25.*

<div style="float: right;">
  <div class="flex flex-wrap space-x-1">
    <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-EE4C2C?logo=pytorch&logoColor=white&style=flat">
  </div>
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## Phi4 Multimodal

[Phi4 Multimodal](https://huggingface.co/papers/2503.01743) is a multimodal model capable of text, image, and speech and audio inputs or any combination of these. It features a mixture of LoRA adapters for handling different inputs, and each input is routed to the appropriate encoder.

You can find all the original Phi4 Multimodal checkpoints under the [Phi4](https://huggingface.co/collections/microsoft/phi-4-677e9380e514feb5577a40e4) collection.

> [!TIP]
> This model was contributed by [cyrilvallez](https://huggingface.co/cyrilvallez).
>
> Click on the Phi-4 Multimodal in the right sidebar for more examples of how to apply Phi-4 Multimodal to different tasks.

The example below demonstrates how to generate text based on an image with [`Pipeline`] or the [`AutoModel`] class.

<hfoptions id="usage">
<hfoption id="Pipeline">

```python
from transformers import pipeline
generator = pipeline("text-generation", model="microsoft/Phi-4-multimodal-instruct", dtype="auto", device=0)

prompt = "Explain the concept of multimodal AI in simple terms."

result = generator(prompt, max_length=50)
print(result[0]['generated_text'])
```

</hfoption>
<hfoption id="AutoModel">

```python
import torch
from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig
from accelerate import Accelerator

model_path = "microsoft/Phi-4-multimodal-instruct"
device = Accelerator().device

processor = AutoProcessor.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device, dtype=torch.float16)

model.load_adapter(model_path, adapter_name="vision", device_map=device, adapter_kwargs={"subfolder": 'vision-lora'})

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://www.ilankelman.org/stopsigns/australia.jpg"},
            {"type": "text", "text": "What is shown in this image?"},
        ],
    },
]

model.set_adapter("vision")
inputs = processor.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

generate_ids = model.generate(
    **inputs,
    max_new_tokens=1000,
    do_sample=False,
)
generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:]
response = processor.batch_decode(
    generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
)[0]
print(f'>>> Response\n{response}')
```

</hfoption>
</hfoptions>

## Notes

The example below demonstrates inference with an audio and text input.

```py
import torch
from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig
from accelerate import Accelerator

model_path = "microsoft/Phi-4-multimodal-instruct"
device = Accelerator().device

processor = AutoProcessor.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device,  dtype=torch.float16)

model.load_adapter(model_path, adapter_name="speech", device_map=device, adapter_kwargs={"subfolder": 'speech-lora'})
model.set_adapter("speech")
audio_url = "https://upload.wikimedia.org/wikipedia/commons/b/b0/Barbara_Sahakian_BBC_Radio4_The_Life_Scientific_29_May_2012_b01j5j24.flac"
messages = [
    {
        "role": "user",
        "content": [
            {"type": "audio", "url": audio_url},
            {"type": "text", "text": "Transcribe the audio to text, and then translate the audio to French. Use <sep> as a separator between the origina transcript and the translation."},
        ],
    },
]

inputs = processor.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

generate_ids = model.generate(
    **inputs,
    max_new_tokens=1000,
    do_sample=False,
)
generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:]
response = processor.batch_decode(
    generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
)[0]
print(f'>>> Response\n{response}')

```

## Phi4MultimodalFeatureExtractor

[[autodoc]] Phi4MultimodalFeatureExtractor

## Phi4MultimodalImageProcessorFast

[[autodoc]] Phi4MultimodalImageProcessorFast

## Phi4MultimodalProcessor

[[autodoc]] Phi4MultimodalProcessor
    - __call__

## Phi4MultimodalAudioConfig

[[autodoc]] Phi4MultimodalAudioConfig

## Phi4MultimodalVisionConfig

[[autodoc]] Phi4MultimodalVisionConfig

## Phi4MultimodalConfig

[[autodoc]] Phi4MultimodalConfig

## Phi4MultimodalAudioModel

[[autodoc]] Phi4MultimodalAudioModel

## Phi4MultimodalVisionModel

[[autodoc]] Phi4MultimodalVisionModel

## Phi4MultimodalModel

[[autodoc]] Phi4MultimodalModel
    - forward

## Phi4MultimodalForCausalLM

[[autodoc]] Phi4MultimodalForCausalLM
    - forward
