Developed a GPU-efficient fine-tuning workflow for large language models within the huggingface/cookbook repository, focusing on accelerating experimentation and optimizing resource usage. Leveraging Python, TRL/Transformers APIs, and LoRA adapters, the solution enabled concurrent training of multiple configurations on a single GPU, achieving 16-24x faster iteration cycles. Integrated TensorBoard for real-time monitoring and introduced Interactive Control Operations to allow mid-training adjustments without full restarts. Additionally, streamlined repository management by migrating cookbook asset hosting to an external HuggingFace dataset, reducing repository size and improving maintainability. The work emphasized practical machine learning engineering, data science, and resource optimization for scalable model development.