Ethereum smart contracts manage billions in digital assets, and vul- nerability detection is critical given the immutability of deployed code and the irreversible nature of transactions. However, exist- ing tools such as Slither rely on rigid, rule-based analysis, and general-purpose language models like ChatGPT often miss rare or context-dependent bugs. To address these limitations, this paper presents BreachT5, an ensemble of two fine-tuned CodeT5+ models designed for multi-label vulnerability detection in Solidity contracts. We first fine-tune a 220M parameter model on over 67,000 real con- tracts labeled with the Smart Contract Weakness Classification (SWC), revealing intrinsic detection differences across vulnerabil- ity types. We then explore the performance of a 770M variant, which improves accuracy on frequent classes but underperforms on rare ones. To balance this trade-off, BreachT5 combines both models via soft voting with per-class thresholds. Our results on the BCCC-SCsVuls2024 dataset show that BreachT5 achieves 0.556 Macro-F1 and 0.612 Micro-F1, outperforming the two standalone models, Slither, and GPT-5 in multi-label vulnerability detection