This project is a development platform for managing the lifecycle of generative artificial intelligence models. It provides a unified environment for accessing, fine-tuning, and deploying large language models, serving as an orchestrator that handles the integration of diverse models into custom applications.
The platform distinguishes itself by offering a managed infrastructure for hosting and scaling models, which removes the requirement for manual server maintenance or configuration. It includes integrated tools for supervised fine-tuning and vector embedding optimization, allowing for the refinement of model performance to meet specialized domain requirements.
The framework incorporates comprehensive capabilities for monitoring and governance, including automated quality evaluation services that use programmatic rubrics to assess output accuracy. It also enforces responsible artificial intelligence standards through policy-driven content filtering, ensuring that generated responses remain aligned with established safety and ethical guidelines.
The repository provides a collection of Jupyter Notebooks that serve as documentation and implementation guides for these development and deployment workflows.
