Memes and other text-embedded images are powerful tools for expressing opinions and identities, especially within marginalized socio-political movements. Detecting hate speech in this type of multimodal content is challenging because of the subtle ways text and visuals interact. In this paper, we describe our approach for Subtask A of the Shared Task on Multimodal Hate Detection in Marginalized Movement@CASE 2025, which focuses on classifying memes as either Hate or No Hate. We tested both unimodal and multimodal setups, using models like DistilBERT, HateBERT, Vision Transformer, and Swin Transformer. Our best system is the large multimodal model Qwen2.5-VL-7B-Instruct-bnb-4bit, fine-tuned with 4-bit quantization and instruction prompts. While we also tried late fusion with multiple transformers, Qwen performed better at capturing text-image interactions in memes. This LLM-based approach reached the highest F1-score of 0.8086 on the test set, ranking our team 5th overall in the task. These results show the value of late fusion and instruction-tuned LLMs for tackling complex hate speech in socio-political memes.