TextBrewer provides a framework for applying knowledge distillation techniques to natural language processing models. It implements various distillation methods from both NLP and computer vision research, enabling practitioners to compress large models (such as BERT-based architectures) with minimal performance loss. The toolkit offers easy-to-use abstractions for configuring student-teacher model relationships and supports standard NLP tasks including text classification and sequence labeling.