We present a unified multi-task fine-tuning system submitted to the WMT 2026 Shared Task on Multitask LLMs with Limited Resources (Ukrainian track), covering machine translation, spelling correction, grammar correction, question answering, and mathematical reasoning. Our system is built on Qwen3.5-2B-Instruct and adopts a two-stage training pipeline that gradually introduces heterogeneous tasks while replaying selected data from earlier stages to mitigate catastrophic forgetting. The resulting model achieves a strong balance across all evaluated tasks, consistently outperforming the official baseline and obtaining the best performance on every task except question answering. Our results demonstrate that staged multi-task fine-tuning is an effective strategy for adapting compact multilingual LLMs under limited-resource constraints.