Fine-tuning Open Source AI Models like LLaMa, Mistral, SDXL involves adapting a pre-trained model to a new and more specific task. Traditional methods for fine-tuning LLMs include preparing the dataset, choosing the finetuning method, setting up the training environment, and finally fine-tuning the model itself. MonsterAPI provides a streamlined finetuning workflow for LoRA/QLoRA-based LLM finetuning, making the process easier and more efficient. Different methods for finetuning include supervised fine-tuning, few-shot learning, task-specific fine-tuning, reinforcement learning from human feedback (RLHF), and parameter-efficient fine-tuning. MonsterAPI helps overcome common challenges with LLM finetuning such as data challenges, hyperparameter tuning, computational bottlenecks, deployment and integration, and time constraints.