vllm-omni/examples/online_serving/ming_flash_omni

Ming-flash-omni 2.0

Installation

Please refer to README.md

Deployment modes

Mode	Launch command	Output
Thinker only (multimodal understanding)	vllm serve ... --omni	Text
Thinker + Talker (omni-speech)	vllm serve ... --omni --stage-configs-path ming_flash_omni.yaml	Text + Audio

For standalone TTS (talker only), see examples/online_serving/ming_flash_omni_tts/.

Run examples (Ming-flash-omni 2.0)

Launch the Server

Thinker only (text output):

vllm serve Jonathan1909/Ming-flash-omni-2.0 --omni --port 8091

Thinker + Talker (omni-speech, text + audio output):

vllm serve Jonathan1909/Ming-flash-omni-2.0 --omni --port 8091 \
    --stage-configs-path vllm_omni/model_executor/stage_configs/ming_flash_omni.yaml

Pass --stage-configs-path /path/to/your_config.yaml to use a custom stage config.

Send Multi-modal Request

Shared Python client (supports text | use_image | use_audio | use_video | use_mixed_modalities; pass --image-path / --audio-path / --video-path for local files or URLs, --modalities text for output, --help for the full flag list):

python examples/online_serving/openai_chat_completion_client_for_multimodal_generation.py \
    --model Jonathan1909/Ming-flash-omni-2.0 \
    --query-type use_mixed_modalities \
    --port 8091 --host localhost \
    --modalities text

Parameterized curl wrapper in this directory:

bash run_curl_multimodal_generation.sh text
bash run_curl_multimodal_generation.sh use_image
bash run_curl_multimodal_generation.sh use_audio
bash run_curl_multimodal_generation.sh use_video
bash run_curl_multimodal_generation.sh use_mixed_modalities

Modality control

modalities	Server config	Output
["text"] or omitted	Thinker only	Text
["audio"]	Thinker + Talker	Audio (speech)
["text", "audio"]	Thinker + Talker	Text + Audio

For ready-to-copy curl examples (text / audio / multimodal input, SSE streaming, reasoning mode), see the recipe at recipes/inclusionAI/Ming-flash-omni-2.0.md.

OpenAI Python SDK — streaming

from openai import OpenAI

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

response = client.chat.completions.create(
    model="Jonathan1909/Ming-flash-omni-2.0",
    messages=[
        {"role": "system", "content": [{"type": "text", "text": "你是一个友好的AI助手。\n\ndetailed thinking off"}]},
        {"role": "user", "content": "请详细介绍鹦鹉的生活习性。"},
    ],
    modalities=["text"],
    stream=True,
)
for chunk in response:
    for choice in chunk.choices:
        if hasattr(choice, "delta") and choice.delta.content:
            print(choice.delta.content, end="", flush=True)
print()

The --stream flag on the Python client script above shows the same pattern driven by the shared multimodal client.