Building an AI Medical Translator for Nepali with LLaMA 3.1
I fine-tuned Meta’s LLaMA 3.1-8B model to translate medical text from English to Nepali, using only a free Google Colab GPU (Tesla T4). The result?An 8.9× performance improvement over zero-shot translation—turning an unusable model into something genuinely helpful for 30 million Nepali speakers. Key highlights: This is a story about access, efficiency, and why cutting-edge medical AI doesn’t have to be locked behind massive budgets. The Problem The Solution A domain-specific AI medical translator built with: This approach enables efficient training, low memory usage, and real-world deployability. How It Was Built Data Training Results Metric Zero-Shot Fine-Tuned BLEU 1.31 11.63 ChrF++ 16.35 34.65 ->Zero-shot translation was unusable.->Fine-tuning made the model practically useful. Example Translations EN: Take two tablets after meals three times daily.NE: दिनमा तीन पटक खाना पछि दुई ट्याब्लेट लिनुहोस्। ✔ Correct dosage✔ Preserved medical terminology Limitations