Worked on enhancing the data preparation pipeline for multimodal chat fine-tuning in the mosaicml/llm-foundry repository. Developed and delivered a feature that validates chat message content, allowing it to be processed as either a string or a list, which improves the robustness and flexibility of multimodal data handling. Focused on data validation and LLM fine-tuning using Python, the implementation refined validation logic to ensure higher data quality and reliability. Maintained clear commit traceability to support future audits and enhancements, contributing to a more maintainable and extensible multimodal data pipeline for chat-enabled model training workflows. No bugs were reported or fixed.