 | 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. I attached a demo below of it running on my 2021 Fire HD 10 tablet which has 3GB of RAM. 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. Model info and download: https://huggingface.co/datasets/mwebazarick/BULaMU [link] [comments] |
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/LocalLLaMA / 3/30/2026
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Key Points
- The article describes BULaMU, a family of small, compute-efficient language models (20M, 47M, and 110M parameters) trained from scratch specifically for the low-resource language Luganda.
- It claims the models can run fully offline on Android phones and tablets without needing a GPU or internet connection, demonstrated on a 2021 Fire HD 10 tablet with 3GB RAM.
- The author built an Android app called E.A.S.T. (Expanding Access to Systems of Learning and Intelligence) that provides on-device interaction with the Luganda models.
- Model downloads and related resources are published via Hugging Face datasets and a GitHub repository linked in the post.
- The project positions itself as part of a broader effort to improve AI access for speakers of low-resource languages and for users on low-power, low-cost devices.
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