In June 2025, Gabriel Sutter developed a configurable cache directory feature for MLIP models in the FAIR-Chem/fairchem repository. He enhanced the core Python APIs, specifically get_predict_unit and get_isolated_atomic_energies, to accept a cache_dir argument, allowing users to explicitly control where pre-trained models are stored and loaded. This update addressed deployment flexibility and reproducibility, making it easier to manage model storage across diverse environments and CI/CD pipelines. Gabriel applied his skills in machine learning, model management, and software engineering to deliver a focused, well-scoped improvement that aligns with user customization needs and reduces operational friction in model deployment.