Worked on the huggingface/torchtitan repository, delivering targeted improvements in deep learning model evaluation and reliability. Developed a flexible metrics processing configuration by introducing model-specific arguments to the metrics processor function, enabling per-model customization and streamlining experimentation workflows. Addressed robustness in parameter counting for models with nested Embedding modules, reducing edge-case errors and improving model introspection for developers and downstream tools. Enhanced type checking by resolving ModelProtocol typing issues, supporting more reliable class method definitions. Leveraged Python, deep learning, and type checking expertise to improve both the flexibility and reliability of the codebase, focusing on maintainability and developer experience.