Moonshine Tiny is the smallest model in Useful Sensors' Moonshine family — a 27M-parameter, sequence-to-sequence English speech-recognition model designed for real-time, on-device transcription on hardware that is severely constrained in memory and compute. Trained on 200,000 hours of audio, it transcribes English speech to text and, despite its size, reports greater accuracy than existing ASR systems of comparable scale on standard benchmarks. It targets developers building live transcription and voice-command experiences on low-cost devices. Like other autoregressive ASR models it can occasionally hallucinate or repeat on very short or clipped segments, so robust in-domain evaluation is recommended before deployment. This OpenASR repo repackages the original weights as .oasr packs that run natively in the OpenASR runtime — no Python at inference time. The q8_0 build is the recommended default (near-reference accuracy at roughly a third of the footprint); fp16 is for verification or maximum fidelity.