huggingface--transformers/docs/source/en/model_doc/phi4_multimodal.md

This model was published in HF papers on 2025-03-03 and contributed to Hugging Face Transformers on 2025-03-25.

Phi4 Multimodal

Phi4 Multimodal 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 collection.

Tip

This model was contributed by 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.

from transformers import pipeline


generator = pipeline("text-generation", model="microsoft/Phi-4-multimodal-instruct", device=0)

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

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

from transformers import AutoModelForCausalLM, AutoProcessor


model_path = "microsoft/Phi-4-multimodal-instruct"

processor = AutoProcessor.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto")

model.load_adapter(model_path, adapter_name="vision", device_map="auto", 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}')

Notes

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

import torch

from transformers import AutoModelForCausalLM, AutoProcessor


model_path = "microsoft/Phi-4-multimodal-instruct"

processor = AutoProcessor.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto")

model.load_adapter(model_path, adapter_name="speech", device_map="auto", 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

Phi4MultimodalImageProcessor

autodoc Phi4MultimodalImageProcessor - preprocess

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