RapidFire AI, now integrated with Hugging Face's TRL, offers a significant enhancement in fine-tuning and post-training large language models by enabling rapid comparison of multiple configurations without substantial code changes or increased GPU requirements. This tool allows users to concurrently launch multiple configurations on a single GPU and compare them in near real-time, thanks to an innovative adaptive, chunk-based scheduling and execution scheme. The integration can deliver 16-24 times higher experimentation throughput than traditional sequential methods, facilitating faster achievement of optimized evaluation metrics. Additionally, RapidFire AI provides live three-way communication between the user's IDE, a metrics dashboard, and a multi-GPU execution backend, with features like interactive control operations allowing real-time adjustments. The system's design focuses on maximizing GPU utilization and reducing time and resource wastage, with benchmarks showing significant speedups in training times. It offers a user-friendly interface with an MLflow-based dashboard and supports further integrations with other popular dashboards, enhancing the efficiency and effectiveness of machine learning workflows.