We present a Hy-MT2-1.8B system for the WMT26 Chinese–Southeast Asian translation task, covering fourteen translation directions between Chinese and seven Southeast Asian languages. The system uses a staged adaptation pipeline consisting of monolingual continued pretraining followed by bilingual supervised fine-tuning. We compare parameter-efficient LoRA SFT with full-parameter SFT from the same continued-pretraining checkpoint under matched data and evaluation conditions. On 26,474 validation examples, full-parameter SFT achieves higher macro-average sacreBLEU and COMET scores than LoRA SFT, although the gains vary across language directions. The implementation separates data construction, model preparation, quality evaluation, and serving so that the submitted system can be reproduced and evaluated under the shared-task conditions.