In the present study we investigated the potential of LLMs as predictive tools for antimicrobial activity and hemolysis of peptide sequences. We assessed that fine-tuning GPT models in cloud is a relatively easy and fast process as access through the API eliminates the need to buy expensive hardware and requires little technical expertise. Duration of fine-tuning was short, and the associated costs were low (Table ). In contrast to cloud-based fine-tuning, local model training involves setting up and maintaining hardware, which can be costly and require technical expertise.