Knowledge distillation, a method that transfers information from a large "teacher" model to a smaller "student" model, has become a fundamental tool for reducing the size and expense of AI systems. Originating from a 2015 Google paper, the technique leverages soft‑target probabilities to convey nuanced relationships between data classes, enabling compact models to retain high performance. Over the years, distillation has been applied to language models such as BERT and its distilled variant, DistilBERT, and is now offered as a service by major cloud providers. Recent developments continue to expand its utility across reasoning tasks and open‑source initiatives. Read more →

Knowledge distillation, a method that transfers information from a large "teacher" model to a smaller "student" model, has become a fundamental tool for reducing the size and expense of AI systems. Originating from a 2015 Google paper, the technique leverages soft‑target probabilities to convey nuanced relationships between data classes, enabling compact models to retain high performance. Over the years, distillation has been applied to language models such as BERT and its distilled variant, DistilBERT, and is now offered as a service by major cloud providers. Recent developments continue to expand its utility across reasoning tasks and open‑source initiatives. Read more →

Knowledge distillation, a method that transfers information from a large "teacher" model to a smaller "student" model, has become a fundamental tool for reducing the size and expense of AI systems. Originating from a 2015 Google paper, the technique leverages soft‑target probabilities to convey nuanced relationships between data classes, enabling compact models to retain high performance. Over the years, distillation has been applied to language models such as BERT and its distilled variant, DistilBERT, and is now offered as a service by major cloud providers. Recent developments continue to expand its utility across reasoning tasks and open‑source initiatives. Read more →

Knowledge distillation, a method that transfers information from a large "teacher" model to a smaller "student" model, has become a fundamental tool for reducing the size and expense of AI systems. Originating from a 2015 Google paper, the technique leverages soft‑target probabilities to convey nuanced relationships between data classes, enabling compact models to retain high performance. Over the years, distillation has been applied to language models such as BERT and its distilled variant, DistilBERT, and is now offered as a service by major cloud providers. Recent developments continue to expand its utility across reasoning tasks and open‑source initiatives. Read more →

Knowledge distillation, a method that transfers information from a large "teacher" model to a smaller "student" model, has become a fundamental tool for reducing the size and expense of AI systems. Originating from a 2015 Google paper, the technique leverages soft‑target probabilities to convey nuanced relationships between data classes, enabling compact models to retain high performance. Over the years, distillation has been applied to language models such as BERT and its distilled variant, DistilBERT, and is now offered as a service by major cloud providers. Recent developments continue to expand its utility across reasoning tasks and open‑source initiatives. Read more →