phi3.md

Phi-3 Vision

Introduction

Phi-3 is a family of small language and multi-modal models from MicroSoft. LMDeploy supports the multi-modal models as below.

Model	Size	Supported Inference Engine
microsoft/Phi-3-vision-128k-instruct	4.2B	PyTorch
microsoft/Phi-3.5-vision-instruct	4.2B	PyTorch

The next chapter demonstrates how to deploy an Phi-3 model using LMDeploy, with microsoft/Phi-3.5-vision-instruct as an example.

Installation

Please install LMDeploy by following the installation guide and install the dependency Flash-Attention

# It is recommended to find the whl package that matches the environment from the releases on https://github.com/Dao-AILab/flash-attention.
pip install flash-attn

Offline inference

The following sample code shows the basic usage of VLM pipeline. For more examples, please refer to VLM Offline Inference Pipeline

from lmdeploy import pipeline
from lmdeploy.vl import load_image

pipe = pipeline('microsoft/Phi-3.5-vision-instruct')

image = load_image('https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg')
response = pipe(('describe this image', image))
print(response)

Online serving

Launch Service

You can launch the server by the lmdeploy serve api_server CLI:

lmdeploy serve api_server microsoft/Phi-3.5-vision-instruct

Integrate with OpenAI

Here is an example of interaction with the endpoint v1/chat/completions service via the openai package. Before running it, please install the openai package by pip install openai

from openai import OpenAI

client = OpenAI(api_key='YOUR_API_KEY', base_url='http://0.0.0.0:23333/v1')
model_name = client.models.list().data[0].id
response = client.chat.completions.create(
    model=model_name,
    messages=[{
        'role':
        'user',
        'content': [{
            'type': 'text',
            'text': 'Describe the image please',
        }, {
            'type': 'image_url',
            'image_url': {
                'url':
                'https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg',
            },
        }],
    }],
    temperature=0.8,
    top_p=0.8)
print(response)