Qwen2.5-VL-72B-Instruct

Visual LLM

Qwen2.5-VL is a powerful vision-language model, designed for advanced image understanding. It can generate detailed image captions, analyze documents, OCR, detect objects, and answer questions based on visuals, making it useful for AI assistants, RAG and Agents.

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Publicado el huggingface

27/01/2025

Licencia: Qwen

Precio de entrada

0.91 € /Mtoken(entrada)

Precio de salida

0.91 € /Mtoken(salida)

Características soportadas

MultimodalStreaming

Formatos de salida

raw_textjson_objectjson_schema

Tamaños de contexto

32k

Parámetros

72B

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Example of how to use Qwen2.5-VL-72B-Instruct Vision Language Model API in python

Introduction

The following examples walk you through the use of a VLM (Vision Language Model), able to take images and texts prompts and generate text. To send a multimodal input to the model, you have to use a content list that will contain the text prompt and the base64 encoded image.

A VLM encodes the image as embeddings and uses tokens to represent and process the image along the usual text tokens, so an image will use some of the input context length of the model.

AI Endpoints VLMs

These Vision Language Model APIs are based on Open-Source models:

Qwen2.5-VL-72B-Instruct: A versatile model that can handle both image and text input, as well as text-only requests
llava-next-mistral-7b: On the other hand, this model is specifically designed to process image and text input together. It cannot process text-only requests. Therefore, it should not be used as a simple LLM.

Please ensure that you choose the appropriate model based on your specific use case.

With a simple HTTP client (requests)

First, install the requests library:

pip install requests

Next, export your access token to the OVH_AI_ENDPOINTS_ACCESS_TOKEN environment variable:

export OVH_AI_ENDPOINTS_ACCESS_TOKEN=<your-access-token>

If you do not have an access token key yet, follow the instructions in the AI Endpoints – Getting Started.

If you don't have any images available for testing, save the following image locally as sample.jpg:

And then run the following example python code to ask the VLM to describe this image (or perform another task by adapting the content field of the messages dictionary):

import mimetypes
import os
import requests
import base64

image_filepath = "sample.jpg"
with open(image_filepath, "rb") as img_file:
    image_data = img_file.read()

# detect MIME type (default to jpeg if unknown)
mime_type, _ = mimetypes.guess_type(image_filepath)
if mime_type is None:
    mime_type = "image/jpeg"

encoded_image = f"data:{mime_type};base64,{base64.b64encode(image_data).decode('utf-8')}"

# You can use the model dedicated URL
url = "https://qwen-2-5-vl-72b-instruct.endpoints.kepler.ai.cloud.ovh.net/api/openai_compat/v1/chat/completions"
# Or our unified endpoint for easy model switching with optimal OpenAI compatibility
url = "https://oai.endpoints.kepler.ai.cloud.ovh.net/v1/chat/completions"
payload = {
    "max_tokens": 512,
    "messages": [
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "Describe this image."
                },
                {
                    "type": "image_url",
                    "image_url": {
                        "url": encoded_image
                    }
                }
            ]
        }
    ],
    "model": "Qwen2.5-VL-72B-Instruct",
    "temperature": 0.2,
}
 
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {os.getenv('OVH_AI_ENDPOINTS_ACCESS_TOKEN')}",
}
 
response = requests.post(url, json=payload, headers=headers)
if response.status_code == 200:
    # Handle response
    response_data = response.json()
    # Parse JSON response
    choices = response_data["choices"]
    for choice in choices:
        text = choice["message"]["content"]
        # Process text and finish_reason
        print(text)
else:
    print("Error:", response.status_code, response.text)

With the Python OpenAI library

The Qwen2.5-VL-72B-Instruct API is compatible with the openai specification.

First install the openai library:

pip install openai

Next, export your access token to the OVH_AI_ENDPOINTS_ACCESS_TOKEN environment variable:

export OVH_AI_ENDPOINTS_ACCESS_TOKEN=<your-access-token>

If you do not have an access token key yet, follow the instructions in the AI Endpoints – Getting Started.

Finally, run the following python code:

import mimetypes
import os
import base64

from openai import OpenAI

# You can use the model dedicated URL
url = "https://qwen-2-5-vl-72b-instruct.endpoints.kepler.ai.cloud.ovh.net/api/openai_compat/v1"
# Or our unified endpoint for easy model switching with optimal OpenAI compatibility
url = "https://oai.endpoints.kepler.ai.cloud.ovh.net/v1"

client = OpenAI(
    base_url=url,
    api_key=os.getenv("OVH_AI_ENDPOINTS_ACCESS_TOKEN")
)

def multimodal_chat_completion(new_message: str, image_filepath: str = None) -> str:
    new_user_message = {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": new_message
            }
        ]
    }
    
    if image_filepath is not None:
        with open(image_filepath, "rb") as img_file:
            image_data = img_file.read()

        # detect MIME type (default to jpeg if unknown)
        mime_type, _ = mimetypes.guess_type(image_filepath)
        if mime_type is None:
            mime_type = "image/jpeg"

        encoded_image = f"data:{mime_type};base64,{base64.b64encode(image_data).decode('utf-8')}"
        image_content = {
            "type": "image_url",
            "image_url": {
                "url": encoded_image
            }
        }
        new_user_message["content"].append(image_content)
    
    history_openai_format = [new_user_message]

    return client.chat.completions.create(
        model="Qwen2.5-VL-72B-Instruct",
        messages=history_openai_format,
        temperature=0.2,
        max_tokens=1024
    ).choices.pop().message.content


if __name__ == '__main__':
    print(multimodal_chat_completion("Describe this image.", "sample.jpg"))

Model rate limit

When using AI Endpoints, the following rate limits apply:

Anonymous: 2 requests per minute, per IP and per model.
Authenticated with an API access key: 400 requests per minute, per Public Cloud project and per model.

If you exceed this limit, a 429 error code will be returned.

If you require higher usage, please get in touch with us to discuss increasing your rate limits.

Qwen2.5-VL-72B-Instruct