Ming-3.0-audio-flash vs Ming-3.0-audio-tiny

Ming-3.0-audio-flash

Ming-3.0-audio-flash is a cousin for Chinese and English transcription, questions about long audio and spoken replies in built-in or cloned voices, under MIT. It needs about 260 GB on a multi-GPU server, with inclusionAI's own runtime branches or Transformers remote code. The main risks are running repository code to load it and no published safety results.

Ming-3.0-audio-tiny

A small MIT-licensed audio-language model that transcribes Chinese and English, answers questions about audio and replies in a built-in or cloned voice. It needs about 25 GB and SGLang-Omni from main or inclusionAI's own vLLM-Omni and SGLang branches. The main risk is that every loading path runs repository code, with nothing published on safety.

Ming-3.0-audio-flash

1. Start it with inclusionAI's recipe, hf download inclusionAI/Ming-3.0-audio-flash --local-dir inclusionAI/Ming-3.0-audio-flash && export MODEL_PATH="$PWD/inclusionAI/Ming-3.0-audio-flash" && pip install -r requirements-vllm-omni.txt && MING_OUTPUT_MODE=asr ./scripts/serve_vllm_omni.sh "$MODEL_PATH", run from a clone of github.com/inclusionAI/Ming-3.0-audio-flash
2. The 128K profile uses tensor parallelism over eight 80 GB H800 GPUs, the SGLang profile four and SGLang-Omni's cookbook two. It doesn't fit the 107.4 GB reference machine
3. Use MING_OUTPUT_MODE=asr with the validated prompt and greedy decoding for transcripts, and split the language label from the text at the tab
4. Use MING_OUTPUT_MODE=text for audio questions and the Talker profile with modalities=["text", "audio"] for spoken replies
5. Send audio as a base64 data URL in an audio_url item for vLLM-Omni, or as input_audio for SGLang
6. Pick a voice with audio.voice and an id, or clone one with ref_audio and its exact prompt_text, never both
7. Turn thinking on per request with chat_template_kwargs {"enable_thinking": true}, it's off by default and the reasoning comes back in think tags
8. Tool calls follow the template's <tool_call> with <arg_key> and <arg_value> pairs, and no parser is named, so parse them yourself
9. Review the repository's Python and the talker/ code, .pyc files included, before loading with trust_remote_code

Ming-3.0-audio-tiny

1. Fastest start is SGLang-Omni from main, sgl-omni serve --model-path inclusionAI/Ming-3.0-audio-tiny --talker.gpu 0, which exposes OpenAI-style /v1/audio/transcriptions, /v1/audio/speech and /v1/chat/completions
2. For transcripts use greedy decoding with 10-gram blocking and the validated ASR prompt, and expect a language label, a tab, then the text
3. Parse tool calls yourself from <tool_call>name with <arg_key> and <arg_value> pairs, since no runtime parser is named
4. Thinking is off by default, turn it on with chat_template_kwargs enable_thinking true and strip the <think> block from the reply yourself
5. In vLLM-Omni pick a voice with audio.voice id from docs/voices.md, and don't combine it with a reference recording
6. Review the repository's Python before passing trust_remote_code, and consider deleting the __pycache__ folders under talker/ so only readable source runs