We employed parameter-efficient fine-tuning with LoRa to reduce the number of trainable parameters . Key parameters include a rank of 8, an alpha of 32, and a dropout rate of 0.05. The model was trained at 16-bit precision. Training occurred on an AWS g6.2xlarge instance with 24GB of GPU memory. More details are in the SI †.