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title: README
emoji: 🔩
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colorTo: green
sdk: static
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<div align="center">
  <img class="block dark:hidden mx-auto" src="https://raw.githubusercontent.com/XylolabsInc/.github/main/profile/assets/mark_light.png" width="80" alt="Xylolabs">
  <img class="hidden dark:block mx-auto" src="https://raw.githubusercontent.com/XylolabsInc/.github/main/profile/assets/mark_dark.png" width="80" alt="Xylolabs">
  <h1>Xylolabs Inc.</h1>
  <p><strong>Edge Physical AI that catches equipment faults and runs robots.</strong></p>
  <p>Sensors, edge AI and robots for port cranes, power plants and factories.</p>
  <a href="https://xylolabs.com/en/"><img src="https://img.shields.io/badge/xylolabs.com-website-0d1117?style=flat-square" alt="Website"></a>
  <a href="https://github.com/XylolabsInc"><img src="https://img.shields.io/badge/GitHub-XylolabsInc-0d1117?style=flat-square&logo=github" alt="GitHub"></a>
  <a href="https://xylolabs.com/en/contact/?type=poc"><img src="https://img.shields.io/badge/Request_a-PoC-0d1117?style=flat-square" alt="Request a PoC"></a>
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## On Hugging Face

Open LLMs quantized for Tenstorrent Tensix NPUs. Each model's linear layers are quantized to TT-NN's
block floating point formats, `bfloat4_b` (BFP4) or `bfloat8_b` (BFP8), with GPTQ, so TT-NN packs the
weights as they are. Every card lists the formats per tensor class and the model's KL and answer check
against bf16, beside llama.cpp's Q4_K_M of the same model.

The code that makes them is [tensix-bfp-gptq](https://github.com/XylolabsInc/tensix-bfp-gptq)
(Apache-2.0).

Collections: [one per model family](https://huggingface.co/Xylolabs/collections).

## What we build

| Platform | What it does |
|---|---|
| [XyloZero](https://xylolabs.com/en/solutions/xylo-zero/) | Edge AI that reads sensor signals with PLC and drive data to catch equipment anomalies early |
| [Battery Fire Early Detection](https://xylolabs.com/en/solutions/battery-early-warning/) | Hears the acoustic precursors of thermal runaway in AGV, AMR and ESS batteries |
| [Autonomy & Robotics](https://xylolabs.com/en/solutions/robotics/) | On-device AI for robot and drone patrols, VLA manipulation learning and port crane autonomy |

Started as signal research at Seoul National University in 2019, incorporated in December 2025.

Not affiliated with Tenstorrent.