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Official sources#On-device
  1. Google Research72

    Google Research uses smartwatch sensors to identify signs of hypertension.

    Google Research published a multimodal AI system AI-PPG-ACC-HTN in Nature, which passively identifies hypertension status without wristband calibration, using only PPG and accelerometer data from consumer-grade smartwatches.

    Why it matters: The study passively identifies hypertension using PPG and accelerometer data from smartwatches, providing quantitative results for screening and confirmation in two stages, with 24-hour dynamic blood pressure monitoring as the gold standard.

  1. Hugging Face Blog72

    Phonon TTS releases on-device speech models, topping multiple metrics on the Hugging Face open TTS leaderboard

    Phonon releases a small TTS model that can run on local devices, without a server and network, offering two versions of about 65 MB and 75 MB, covering English, French, German, Spanish, and Portuguese.

    Why it matters: The original text outlines the specifications, open-source license, and leaderboard performance of on-device TTS models, allowing judgment on the usability boundary of small models in local voice scenarios.

  1. Hugging Face Blog76

    Liquid AI releases LFM3 series: Same 2B backbone derived text, visual, audio, multimodal and decision models

    Liquid AI releases the new generation Liquid Foundation Models, LFM3, deriving six 2B-level models from the shared backbone LFM3-1.7B-Base, covering text, vision, audio, multimodal, and decision, all open weights on Hugging Face.

    Why it matters: Liquid AI derives six modality models from the same 2B-level backbone and provides single-card fine-tuning and edge deployment paths, which can be used to judge the specialization route for small models.

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