The increased sharing of memes on social media creates serious challenges for automated moderation, especially in low-resource and code-mixed languages such as Nepali. In this paper, we present our system for the CHiPSAL 2026 Shared Task on Multimodal Hate and Sentiment Understanding in Low-Resource Memes. We propose a late-fusion hybrid architecture that combines OpenAI’s Vision Transformer (CLIP ViT-B/32) with a domain-specific Nepali language model (NepBERTa) to capture both visual features and linguistic information. To address data scarcity, we introduce a cross-task label mapping and data augmentation strategy between the hate speech and sentiment datasets. By applying controlled hyperparameter settings and balanced loss optimization, our framework achieved a Macro F1 score of 0.8052 on Subtask A (Hate Speech Detection) and 0.6881 on Subtask B (Sentiment Analysis) in the official CodaBench evaluation, demonstrating the effectiveness of the proposed multimodal approach.