"Qwen2.5-Omni Complete Guide: Alibaba's Multimodal AI Model for Developers"

# Install the DashScope SDK
pip install dashscope

pip install openai

# Requires ~140GB VRAM for the full 72B model
pip install transformers accelerate
# Or use vLLM for production serving
pip install vllm

from openai import OpenAI
import base64

client = OpenAI(
    api_key="your-crazyrouter-key",
    base_url="https://api.crazyrouter.com/v1"
)

# Analyze an image with text prompt
response = client.chat.completions.create(
    model="qwen2.5-omni",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "What's happening in this image? Describe in detail."
                },
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://example.com/photo.jpg"
                    }
                }
            ]
        }
    ],
    max_tokens=1024
)

print(response.choices[0].message.content)

import base64

# Read audio file
with open("meeting_recording.mp3", "rb") as f:
    audio_b64 = base64.b64encode(f.read()).decode()

response = client.chat.completions.create(
    model="qwen2.5-omni",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "Transcribe this audio and provide a summary of the key points discussed."
                },
                {
                    "type": "input_audio",
                    "input_audio": {
                        "data": audio_b64,
                        "format": "mp3"
                    }
                }
            ]
        }
    ],
    max_tokens=2048
)

print(response.choices[0].message.content)

# Analyze a video file
response = client.chat.completions.create(
    model="qwen2.5-omni",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "Watch this video and answer: What product is being demonstrated? What are its key features?"
                },
                {
                    "type": "video_url",
                    "video_url": {
                        "url": "https://example.com/product-demo.mp4"
                    }
                }
            ]
        }
    ],
    max_tokens=1024
)

print(response.choices[0].message.content)

import OpenAI from 'openai';
import fs from 'fs';

const client = new OpenAI({
  apiKey: 'your-crazyrouter-key',
  baseURL: 'https://api.crazyrouter.com/v1'
});

// Image analysis
const response = await client.chat.completions.create({
  model: 'qwen2.5-omni',
  messages: [
    {
      role: 'user',
      content: [
        { type: 'text', text: 'Describe this architecture diagram and identify potential bottlenecks.' },
        {
          type: 'image_url',
          image_url: { url: 'https://example.com/architecture.png' }
        }
      ]
    }
  ],
  max_tokens: 1024
});

console.log(response.choices[0].message.content);

# Customer sends a photo of a damaged product + voice message
response = client.chat.completions.create(
    model="qwen2.5-omni",
    messages=[
        {"role": "system", "content": "You are a helpful customer support agent."},
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "I received this damaged item"},
                {"type": "image_url", "image_url": {"url": "damage_photo.jpg"}},
                {"type": "input_audio", "input_audio": {"data": voice_msg_b64, "format": "wav"}}
            ]
        }
    ]
)

response = client.chat.completions.create(
    model="qwen2.5-omni",
    messages=[
        {"role": "system", "content": "Review this video for content policy violations. Check for: violence, explicit content, misinformation, copyright issues."},
        {
            "role": "user",
            "content": [
                {"type": "video_url", "video_url": {"url": video_url}}
            ]
        }
    ]
)

response = client.chat.completions.create(
    model="qwen2.5-omni",
    messages=[
        {"role": "system", "content": "Summarize this meeting. Include: attendees, key decisions, action items, and any data shown on screen."},
        {
            "role": "user",
            "content": [
                {"type": "video_url", "video_url": {"url": "meeting_recording.mp4"}}
            ]
        }
    ]
)