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| title: README | |
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| <h1>Brainquiver</h1> | |
| <p><b>In the pursuit of intelligence in the palm of our hands.</b></p> | |
| <p> | |
| <a href="https://www.brainquiver.ai">Website</a> · | |
| <a href="https://www.discoverauri.com">AURI</a> · | |
| <a href="mailto:hello@brainquiver.ai">Contact</a> | |
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| --- | |
| Brainquiver is rethinking where language models run. We design efficient architectures that fit on microcontrollers smaller than a fingernail, bringing genuine intelligence to the edge. | |
| The result is AI that is faster, private, and far more sustainable, drawing a fraction of the energy with no water or data-center footprint, bringing capabilities within reach of the people and places centralized infrastructure has left behind, offline, off-grid, and low-cost. | |
| ### AURI | |
| AURI is a family of language models compact enough to run on a single chip, yet capable enough to feel like far more, calling on only the part of itself each moment needs, like stars lighting up to form a constellation. No cloud, no in-flight data, no waiting. Just a mind that lives on the device and answers the instant you ask. | |
| ### What you will find here | |
| - AURI checkpoints - compact, edge-deployable language models | |
| - Quantized weights and conversion tooling for microcontroller targets | |
| - Notes and reproducible configs for embedded inference | |
| ### Status | |
| Early and active. Follow the org to see models as they are released. | |
| <div align="center"><sub>Brainquiver</sub></div> |