Hope speech detection appears to have an essential role to play in fostering positive and inclusive communication on social media, especially in low-resource multilingual settings. This paper describes the system submitted by Team Oryu for Task 1: Coarse-Grained Hope Tone Classification in Code-Mixed Tulu. The task involves classifying comments in social media texts into one of the four classes: Encouraging, Discouraging, Uninvolved, and Blended Tone. The texts in this task show heavy code-mixing between Tulu, English, and Kannada. In order to overcome this challenge, we employed a fine-tuned multilingual transformer model, code-mixed text processing, data augmentation, and class-weighted loss to handle class imbalance. Our proposed system achieved a Macro F1-score of 63%, securing 3rd position on the shared task. The results demonstrate the efficacy of multilingual transformer models in emotionally nuanced classification in code-mixed environments while underscoring the difficulties in capturing blended emotional tones.