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| title: README |
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| # Broadnet Technologies |
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| **Enterprise AI that actually works. Built and run on our own infrastructure.** |
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| We run our own inference for Arabic and English businesses and publish what we measure, under our own name, with a DOI on every |
| paper. Every model here comes with the paper and the evidence behind its numbers. |
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| - **Gemma 4 26B A4B Uno pilot adapter** — the first Uno adapter for a mixture-of-experts model, lossless, with its serving profile and |
| measurements on the card. Paper: [10.5281/zenodo.22820511](https://doi.org/10.5281/zenodo.22820511). |
| - **Uno for vLLM** — self-drafting speculative decoding with no separate draft model, validated on Ampere, Hopper and Blackwell. |
| Code and container: [github.com/brntech/vllm-uno](https://github.com/brntech/vllm-uno). |
| - **Uno in vLLM** (Qwen3-8B), the dense-target study: [10.5281/zenodo.22652609](https://doi.org/10.5281/zenodo.22652609). |
| - Sensitive-data safety: [The Safety Map](https://doi.org/10.5281/zenodo.19688431), 39 models from 14 labs. |
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| Inference API: [inference.broadnet.ai](https://inference.broadnet.ai) · Research: [broadnet.ai/research](https://www.broadnet.ai/research) |
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| Also from Broadnet: [DialerBee](https://dialerbee.com), the AI outbound dialer with answering-machine detection in 11 languages, and |
| [broadnet.me](https://www.broadnet.me), our telecom and omnichannel messaging (bulk SMS, A2P, HLR lookup, SMS firewall). |
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