This paper explores the potential of using fine- tuned Large Language Models (LLMs) for generating counter-narratives (CNs) to combat hate speech (HS). We focus on English and Basque, leveraging the ML_MTCONAN_KN dataset, which provides hate speech and counter-narrative pairs in multiple languages. Our study compares the performance of Mis- tral, Llama, and a Llama-based LLM fine- tuned on a Basque language dataset for CN generation. The generated CNs are evalu- ated using JudgeLM (a LLM to evaluate other LLMs in open-ended scenarios) along with traditional metrics such as ROUGE-L, BLEU, BERTScore, and other traditional metrics. The results demonstrate that fine-tuned LLMs can produce high-quality contextually relevant CNs for low-resource languages that are comparable to human-generated responses, offering a sig- nificant contribution to combating online hate speech across diverse linguistic settings.