# Qwen3-Omni

## 🛠️ Installation

Please refer to [README.md](../../../README.md)

## Run examples (Qwen3-Omni)

### Launch the Server

```bash
vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091
```

If you want to open async chunking for qwen3-omni, launch the server with command below

```bash
vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091 --stage-configs-path /vllm_omni/model_executor/stage_configs/qwen3_omni_moe_async_chunk.yaml
```

If you have custom stage configs file, launch the server with command below
```bash
vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091 --stage-configs-path /path/to/stage_configs_file
```

### Send Multi-modal Request

Get into the example folder
```bash
cd examples/online_serving/qwen3_omni
```

####  Send request via python

```bash
python openai_chat_completion_client_for_multimodal_generation.py --query-type use_image
```

The Python client supports the following command-line arguments:

- `--query-type` (or `-q`): Query type (default: `use_video`). Options: `text`, `use_audio`, `use_image`, `use_video`
- `--model` (or `-m`): Model name/path (default: `Qwen/Qwen3-Omni-30B-A3B-Instruct`)
- `--video-path` (or `-v`): Path to local video file or URL. If not provided and query-type is `use_video`, uses default video URL. Supports local file paths (automatically encoded to base64) or HTTP/HTTPS URLs. Example: `--video-path /path/to/video.mp4` or `--video-path https://example.com/video.mp4`
- `--image-path` (or `-i`): Path to local image file or URL. If not provided and query-type is `use_image`, uses default image URL. Supports local file paths (automatically encoded to base64) or HTTP/HTTPS URLs and common image formats: JPEG, PNG, GIF, WebP. Example: `--image-path /path/to/image.jpg` or `--image-path https://example.com/image.png`
- `--audio-path` (or `-a`): Path to local audio file or URL. If not provided and query-type is `use_audio`, uses default audio URL. Supports local file paths (automatically encoded to base64) or HTTP/HTTPS URLs and common audio formats: MP3, WAV, OGG, FLAC, M4A. Example: `--audio-path /path/to/audio.wav` or `--audio-path https://example.com/audio.mp3`
- `--prompt` (or `-p`): Custom text prompt/question. If not provided, uses default prompt for the selected query type. Example: `--prompt "What are the main activities shown in this video?"`


For example, to use a local video file with custom prompt:

```bash
python openai_chat_completion_client_for_multimodal_generation.py \
    --query-type use_video \
    --video-path /path/to/your/video.mp4 \
    --prompt "What are the main activities shown in this video?"
```

####  Send request via curl

```bash
bash run_curl_multimodal_generation.sh use_image
```


### FAQ

If you encounter error about backend of librosa, try to install ffmpeg with command below.
```
sudo apt update
sudo apt install ffmpeg
```

## Modality control
You can control output modalities to specify which types of output the model should generate. This is useful when you only need text output and want to skip audio generation stages for better performance.

### Supported modalities

| Modalities | Output |
|------------|--------|
| `["text"]` | Text only |
| `["audio"]` | Text + Audio |
| `["text", "audio"]` | Text + Audio |
| Not specified | Text + Audio (default) |

### Using curl

#### Text only

```bash
curl http://localhost:8091/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Qwen/Qwen3-Omni-30B-A3B-Instruct",
    "messages": [{"role": "user", "content": "Describe vLLM in brief."}],
    "modalities": ["text"]
  }'
```

#### Text + Audio

```bash
curl http://localhost:8091/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Qwen/Qwen3-Omni-30B-A3B-Instruct",
    "messages": [{"role": "user", "content": "Describe vLLM in brief."}],
    "modalities": ["audio"]
  }'
```

### Using Python client

```bash
python openai_chat_completion_client_for_multimodal_generation.py \
    --query-type use_image \
    --modalities text
```

### Using OpenAI Python SDK

#### Text only

```python
from openai import OpenAI

client = OpenAI(base_url="http://localhost:8091/v1", api_key="EMPTY")

response = client.chat.completions.create(
    model="Qwen/Qwen3-Omni-30B-A3B-Instruct",
    messages=[{"role": "user", "content": "Describe vLLM in brief."}],
    modalities=["text"]
)
print(response.choices[0].message.content)
```

#### Text + Audio

```python
from openai import OpenAI

client = OpenAI(base_url="http://localhost:8091/v1", api_key="EMPTY")

response = client.chat.completions.create(
    model="Qwen/Qwen3-Omni-30B-A3B-Instruct",
    messages=[{"role": "user", "content": "Describe vLLM in brief."}],
    modalities=["audio"]
)
# Response contains two choices: one with text, one with audio
print(response.choices[0].message.content)  # Text response
print(response.choices[1].message.audio)    # Audio response
```

## Streaming Output
If you want to enable streaming output, please set the argument as below. The final output will be obtained just after generated by corresponding stage. Now we only support text streaming output. Other modalities can output normally.
```bash
python openai_chat_completion_client_for_multimodal_generation.py \
    --query-type use_image \
    --stream
```

## Run Local Web UI Demo

This Web UI demo allows users to interact with the model through a web browser.

### Running Gradio Demo

The Gradio demo connects to a vLLM API server. You have two options:

#### Option 1: One-step Launch Script (Recommended)

The convenience script launches both the vLLM server and Gradio demo together:

```bash
./run_gradio_demo.sh --model Qwen/Qwen3-Omni-30B-A3B-Instruct --server-port 8091 --gradio-port 7861
```

This script will:
1. Start the vLLM server in the background
2. Wait for the server to be ready
3. Launch the Gradio demo
4. Handle cleanup when you press Ctrl+C

The script supports the following arguments:
- `--model`: Model name/path (default: Qwen/Qwen3-Omni-30B-A3B-Instruct)
- `--server-port`: Port for vLLM server (default: 8091)
- `--gradio-port`: Port for Gradio demo (default: 7861)
- `--stage-configs-path`: Path to custom stage configs YAML file (optional)
- `--server-host`: Host for vLLM server (default: 0.0.0.0)
- `--gradio-ip`: IP for Gradio demo (default: 127.0.0.1)
- `--share`: Share Gradio demo publicly (creates a public link)

#### Option 2: Manual Launch (Two-Step Process)

**Step 1: Launch the vLLM API server**

```bash
vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091
```

If you have custom stage configs file:
```bash
vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091 --stage-configs-path /path/to/stage_configs_file
```

**Step 2: Run the Gradio demo**

In a separate terminal:

```bash
python gradio_demo.py --model Qwen/Qwen3-Omni-30B-A3B-Instruct --api-base http://localhost:8091/v1 --port 7861
```

Then open `http://localhost:7861/` on your local browser to interact with the web UI.

The gradio script supports the following arguments:

- `--model`: Model name/path (should match the server model)
- `--api-base`: Base URL for the vLLM API server (default: http://localhost:8091/v1)
- `--ip`: Host/IP for Gradio server (default: 127.0.0.1)
- `--port`: Port for Gradio server (default: 7861)
- `--share`: Share the Gradio demo publicly (creates a public link)
