This article describes the fine-tuning and incremental retraining process of the massive NLLB-200 model applied to the Quechua (Chanka and Collao variants) and Spanish language pair. Using a curated dataset of 22,891 parallel pairs, a robust cleaning strategy and optimized training for consumer hardware (NVIDIA RTX 3060) were implemented. The results demonstrate a progressive improvement in the BLEU metric, reaching a competitive state for translation tasks in low-resource scenarios, in line with the challenges posed by the IWSLT 2026 shared task.