Update vLLM-Omni serving usage in README

README.md

@@ -33,7 +33,7 @@ tags:
 
 This repository contains **Ming-3.0-tts-tiny**, the Ming-3.0-tts mixture-of-experts checkpoint built on Ling V3. Its Ling V3 backbone has 7.9B total parameters and activates about 1.3B parameters per token. The repository includes sharded weights, supporting model code, audio tokenizer, speaker encoder and text tokenizer.
 
-> **Inference requires the [Ming-3.0-tts GitHub repository](https://github.com/inclusionAI/Ming-3.0-tts).** This Hugging Face repository is for checkpoint distribution and does not provide a standalone inference setup. Clone the GitHub repository and follow its README for the current dependencies, inference code and usage instructions. The CLI scripts and Python APIs referenced below are provided by the GitHub repository.
+> The reference CLI scripts and Python APIs below use the [Ming-3.0-tts GitHub repository](https://github.com/inclusionAI/Ming-3.0-tts). Clone it and follow its README for the reference inference environment. For OpenAI-compatible serving, see [vLLM-Omni](#serving-with-vllm-omni) or [SGLang-Omni](#serving-with-sglang-omni) below.
 
 ## Model Details
 
@@ -140,6 +140,35 @@ See the [Tiny examples](https://github.com/inclusionAI/Ming-3.0-tts#ming-30-tts-
 python tools/preflight_tiny.py --model_path /path/to/Ming-3.0-tts-tiny
 ```
 
+## Serving with vLLM-Omni
+
+For installation, follow the [vLLM-Omni official installation guide](https://docs.vllm.ai/projects/vllm-omni/en/latest/getting_started/installation/); [vLLM-Omni](https://github.com/vllm-project/vllm-omni) serves this checkpoint through the OpenAI-compatible `/v1/audio/speech` endpoint. The current recipe targets vLLM 0.32.0. Use a separate environment from the reference inference setup above.
+
+Start the server in one terminal:
+
+```bash
+vllm serve inclusionAI/Ming-3.0-tts-tiny --omni --port 8000
+```
+
+The HF model ID selects the two-stage Ming3-TTS pipeline automatically and downloads the weights and required assets on first launch. Both stages share GPU 0. A complete local model directory may also be used; pass `--revision <HF-commit>` to pin a remote checkpoint.
+
+After the server is ready, run this reference-free voice design example in another terminal:
+
+```bash
+curl --fail-with-body --silent --show-error http://127.0.0.1:8000/v1/audio/speech \
+  -H 'Content-Type: application/json' \
+  -d '{
+    "model": "inclusionAI/Ming-3.0-tts-tiny",
+    "input": "傍晚的风从山口吹过来，远处的云慢慢染成了金色。",
+    "instructions": "{\"风格\":\"年轻女声，声音清晰自然。\"}",
+    "response_format": "wav",
+    "extra_params": {"temperature": 0.0, "ming3_cfg": 1.0},
+    "seed": 1234
+  }' --output ming3_tiny_design.wav
+```
+
+The output is a mono 44.1 kHz WAV. Voice cloning uses `ref_audio` and its matching transcript in `ref_text`; speech controls use `instructions`. For voice cloning, dialect and emotion controls, built-in voices, multi-speaker Podcast, and PCM streaming, see the upstream [Ming-3.0-TTS recipe](https://github.com/vllm-project/vllm-omni/blob/main/recipes/inclusionAI/Ming-3.0-TTS.md).
+
 ## Serving with SGLang-Omni
 
 [SGLang-Omni](https://sgl-project.github.io/sglang-omni/) serves this checkpoint on one GPU: