Developed end-to-end multimodal vision-language model finetuning capabilities across the togethercomputer/together-python and togethercomputer/openapi repositories, enabling training on datasets that combine vision and language data. The work introduced robust validation checks and new constants to ensure the integrity of multimodal inputs, reducing the risk of misconfiguration during model training. API and OpenAPI specification alignment was achieved by updating parameter references to reflect evolving requirements, supporting scalable experimentation and deployment. Leveraged Python and YAML for implementation, with a focus on API development, data validation, and machine learning model training workflows to streamline the integration of multimodal finetuning features into production environments.