  The emergence of large-scale pre-trained models has heightened their
application in various downstream tasks, yet deployment is a challenge in
environments with limited computational resources. Knowledge distillation has
emerged as a solution in such scenarios, whereby knowledge from large teacher
models is transferred into smaller student' models, but this is a non-trivial
process that traditionally requires technical expertise in AI/ML. To address
these challenges, this paper presents InFiConD, a novel framework that
leverages visual concepts to implement the knowledge distillation process and
enable subsequent no-code fine-tuning of student models. We develop a novel
knowledge distillation pipeline based on extracting text-aligned visual
concepts from a concept corpus using multimodal models, and construct highly
interpretable linear student models based on visual concepts that mimic a
teacher model in a response-based manner. InFiConD's interface allows users to
interactively fine-tune the student model by manipulating concept influences
directly in the user interface. We validate InFiConD via a robust usage
scenario and user study. Our findings indicate that InFiConD's
human-in-the-loop and visualization-driven approach enables users to
effectively create and analyze student models, understand how knowledge is
transferred, and efficiently perform fine-tuning operations. We discuss how
this work highlights the potential of interactive and visual methods in making
knowledge distillation and subsequent no-code fine-tuning more accessible and
adaptable to a wider range of users with domain-specific demands.
