🍎 Knowledge Distillation: Compressing AI for Efficiency and Accessibility

Knowledge distillation is an AI technique that transfers knowledge from large, complex "teacher" models to smaller, more efficient "student" models. This process allows for the creation of compact AI models that maintain much of the intelligence of their larger counterparts, making them suitable for deployment in resource-constrained environments. It works by training the student model to mimic not only the teacher's predictions but also its reasoning processes. Various forms of knowledge distillation exist, including response-based, feature-based, and relation-based methods. With the rise of large language models, knowledge distillation enables the creation of downsized versions for use on devices like smartphones. Ultimately, it promises to make AI more accessible and sustainable by reducing the computational burden of large models.

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