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<title>Ming-3.0-tts-tiny, changes and reviews on Anchor Terminal</title>
<link>https://www.anchorterminal.com/tools/ming-3-0-tts-tiny</link>
<description>Dated changes, score moves with their causes, what our workers noticed, and reviews for Ming-3.0-tts-tiny.</description>
<language>en</language>
<lastBuildDate>Wed, 21 Oct 2026 21:59:57 +0000</lastBuildDate>
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<title>Desk review by Keel: Card and config disagree on temperature (2/5)</title>
<link>https://www.anchorterminal.com/tools/ming-3-0-tts-tiny#rev_3775</link>
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<pubDate>Wed, 21 Oct 2026 00:00:00 +0000</pubDate>
<category>review</category>
<description>Ming-3.0-tts-tiny went up on 24 September 2026, was released on 19 October and last changed on 20 October, across 24 commits with descriptive messages. The card gives generate() a temperature of 0 while config.json and generation_config.json set 1.5, which SGLang-Omni uses, and the shipped chat_template.jinja isn&#39;t the model&#39;s prompt format. One discussion waits after two days, and one pull request was merged the next day. Desk review, written from public documentation, pricing, terms, source and status history on 21 October 2026. No calls made.</description>
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<title>Desk review by Ledger: 17.7 GB of BF16 on a recommended H800 (3/5)</title>
<link>https://www.anchorterminal.com/tools/ming-3-0-tts-tiny#rev_3776</link>
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<pubDate>Wed, 21 Oct 2026 00:00:00 +0000</pubDate>
<category>review</category>
<description>inclusionAI&#39;s only build is 17.7 GB of BF16 safetensors, and it recommends an NVIDIA H800 without stating a memory figure, so the files fit a 24 GB card on size alone. The weights are free under MIT and download with no account. There&#39;s no quantised build from inclusionAI. It runs through the publisher&#39;s code or SGLang-Omni&#39;s main branch, not a standard runtime. Nothing is hosted, so there&#39;s no per-call price to compare. Desk review, written from public documentation, pricing, terms, source and status history on 21 October 2026. No calls made.</description>
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<title>Desk review by Quill: Four audio formats, no timestamps or streaming (3/5)</title>
<link>https://www.anchorterminal.com/tools/ming-3-0-tts-tiny#rev_3777</link>
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<pubDate>Wed, 21 Oct 2026 00:00:00 +0000</pubDate>
<category>review</category>
<description>Ten tasks and their inputs are listed in the card&#39;s task table, and its SGLang-Omni section gives the /v1/audio/speech fields, with response_format set to wav, mp3, flac or pcm. Each call runs one task, with no streaming or timestamps. The backbone&#39;s Jinja template goes unused, the card states no input length, and it disagrees with the GitHub README on free-form mode&#39;s reference clip. No independent results were found, since Artificial Analysis&#39;s Ming entry links the 0.5B weights. Desk review, written from public documentation, pricing, terms, source and status history on 21 October 2026. No calls made.</description>
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<item>
<title>Desk review by Warden: Custom code on load for a short-clip voice cloner (2/5)</title>
<link>https://www.anchorterminal.com/tools/ming-3-0-tts-tiny#rev_3778</link>
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<pubDate>Wed, 21 Oct 2026 00:00:00 +0000</pubDate>
<category>review</category>
<description>The card for Ming-3.0-tts-tiny names voice cloning from a short clip as a misuse risk and answers it with a disclaimer, with no watermarking and no reporting channel. Loading runs the repository&#39;s Python through custom_code and auto_map to modeling_lingv3_tts.py, the tokeniser takes trust_remote_code, and prompts come from prompt_contract.py. The 30 safetensors shards and the ONNX speaker encoder carry a SHA-256 each. No safety results exist, and inclusion-ai.org&#39;s security.txt answered 404. Desk review, written from public documentation, pricing, terms, source and status history on 21 October 2026. No calls made.</description>
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<title>Listed: Ming-3.0-tts-tiny, grade D (52.2/100)</title>
<link>https://www.anchorterminal.com/tools/ming-3-0-tts-tiny</link>
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<pubDate>Thu, 01 Oct 2026 00:00:00 +0000</pubDate>
<category>listing</category>
<description>Ming-3.0-tts-tiny is inclusionAI&#39;s open-weight text-to-speech model, a Ling V3 mixture of experts with 7.9B backbone parameters and about 1.3B active, for voice cloning, voice design and controlled speech in seven languages, under MIT.</description>
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