This paper describes our submission to thedimABSA Shared Task (Subtask 1), whichrequires predicting continuous Valence andArousal scores for target aspects in multilin-gual reviews. We evaluate three approaches:prompting-based baselines, a multilingual en-coder model, and a decoder-only LLM withsupervised fine-tuning. Our main focus isefficient adaptation under multilingual datascarcity. We show that compact encoder anddecoder models, when properly fine-tuned,achieve strong performance across languagesand domains. To improve training stability andenforce valid predictions, we use a boundedregression formulation that maps outputs to thetarget score range. We also explore parameter-efficient fine-tuning and intermediate trainingon external affective data. Results show thatprompting-based baselines are substantiallyweaker than supervised models. The mul-tilingual encoder provides a strong and effi-cient baseline, while the best performance isachieved by a compact decoder model withparameter-efficient fine-tuning. Overall, ourfindings highlight the importance of carefulfine-tuning and training design for multilingualdimensional sentiment analysis.