The GPT-J model, with its 6 billion parameters, was fine-tuned using MonsterAPI's MonsterTuner and the Alpaca GPT-4 dataset. The fine-tuning process simplified by MonsterAPI's agentic pipeline allowed users to tailor the pre-trained model to specific tasks in just three simple clicks, eliminating hours or days of complicated processes and associated costs. The vicgalle/alpaca-gpt4 dataset focuses on English instruction-following and is designed for fine-tuning language models. Fine-tuning enables developers to enhance a model's performance by making it more accurate, context-aware, and aligned with the target application. However, challenges such as complex setups, memory constraints, GPU costs, and lack of standardized methodologies can hinder the process. MonsterAPI has addressed these challenges by providing a user-friendly interface that simplifies the setup, optimizes memory utilization, offers low-cost GPU access, and provides a standardized workflow. To get started with fine-tuning an LLM like GPT-J, users can select a language model, upload their dataset, specify hyperparameters, review and submit the finetuning job. The results showed that the fine-tuned model outperformed the base model in all benchmarks and was made available for download from Hugging Face. The cost analysis revealed that MonsterAPI's LLM Finetuner is 1.8x more cost-effective compared to traditional cloud alternatives.