Developed an end-to-end GPT fine-tuning workflow for the Lightning-AI/litgpt repository, focusing on enabling rapid experimentation with minimal setup. Delivered a new Python script that implements the core training loop, including data loading, model initialization, optimization, and validation, while intentionally omitting advanced configurations such as checkpointing and logging to streamline the process. Leveraged deep learning and natural language processing techniques using PyTorch to provide a reproducible and accessible starting point for model fine-tuning. The work emphasized simplicity and reproducibility, laying the groundwork for future enhancements and facilitating experimentation without introducing unnecessary complexity or configuration overhead. No bugs were reported.