The detection of abusive Tamil text using large language models (LLMs) has received relatively little attention compared to BERT variations. We empirically evaluated four families of open-weight LLMs —Gemma, LLaMA, Qwen, and DeepSeek-Distilled— on the Tamil dataset provided by the shared task. The models are assessed under two in-context learning settings (zero-shot and few-shot) and a parameter-efficient fine-tuning approach using LoRA, with model sizes of approximately 2B and 8B parameters. Experimental results show that 8B models consistently outperform their 2B counterparts, indicating the benefit of increased model capacity. Among the adaptation techniques, LoRA fine-tuning significantly outperforms both zero-shot and few-shot prompting. Across all evaluated settings, Google’s Gemma-2-9B model with LoRA fine-tuning achieved the best performance compared to the other model families and our test result was ranked 12th among all 22 submissions with the 0.7959 f1-score.