[P] I trained a language model from scratch for a low resource language and got it running fully on-device on Android (no GPU, demo)

Reddit r/MachineLearning / 3/31/2026

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The project BULaMU trains small, compute-efficient language models from scratch for the low-resource language Luganda, with sizes of 20M, 47M, and 110M parameters.
The author reports the models can run fully offline on an Android phone without a GPU, demonstrating on-device inference constraints being addressed.
An accompanying Android app, E.A.S.T. (Expanding Access to Systems of Learning and Intelligence), enables direct interaction with the models on-device.
The work is framed as improving AI accessibility for speakers of low-resource languages and for users on low-power, low-cost devices.
Resources are shared via GitHub (EAST app), Hugging Face (BULaMU dataset/models), and a linked model whitepaper on Zenodo.

Hi Everybody! I just wanted to share an update on a project I’ve been working on called BULaMU, a family of language models trained (20M, 47M, and 110M parameters) trained entirely from scratch for a low resource language, Luganda. The models are small and compute-efficient enough to run offline on a phone without requiring a GPU or internet connection. I recently built an Android app called E.A.S.T. (Expanding Access to Systems of Learning and Intelligence) that allows you to interact with the models directly on-device. It is available on my GitHub page. This is part of a broader effort to make artificial intelligence more accessible to speakers of low-resource languages and to people using low-power, low-cost devices.

Demo: https://x.com/mwebazarick/status/2038384599320170760?s=46

GitHub: https://github.com/mwebazarick/EAST

Huggingface: https://huggingface.co/datasets/mwebazarick/BULaMU

Model Whitepaper: https://zenodo.org/records/17271688

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