The Haystack framework now provides tools for knowledge distillation in natural language processing (NLP) tasks. Knowledge distillation is a technique that involves transferring knowledge from a larger, more complex model to a smaller, more efficient one. This can be done by using a teacher-student paradigm, where the "teacher" model is the large, complex model and the "student" model is the smaller, more efficient one. The goal of knowledge distillation is to reduce the size and computational cost of the model while maintaining its accuracy and performance. By leveraging this technique, developers can build faster and more efficient models that are better suited for deployment on mobile devices or other resource-constrained systems.