Developer Deploys Offline Medical AI for Four Nigerian Languages After Three Crashes

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A developer successfully deployed a fine-tuned large language model offline on a mid-range Android phone to answer medical questions in Yoruba, Hausa, Igbo, and Nigerian Pidgin, targeting users without reliable internet access. The model, based on Meta's Llama 3.2 3B, was trained on 3,917 medical question-answer pairs using QLoRA fine-tuning and direct preference optimization. Converting the model to the on-device GGUF format triggered three separate crashes, each caused by tokenizer misconfigurations, including a wrong tokenizer type field, faulty auto-detection logic, and a missing BPE merges table. All three issues were resolved through manual fixes, allowing the model to run successfully on-device. However, an unresolved problem remains: for a subset of Yoruba and Igbo prompts, the model generates fluent-sounding but grammatically incorrect and fabricated text, raising concerns about reliability for real-world medical use.

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