Knowledge Distillation: New Methods to Enhance Scalability
New methods in knowledge distillation are reducing costs and enhancing scalability for large language models.
Knowledge distillation is the process of transferring the performance of a large model to a smaller one. Recent advancements have significantly reduced the costs associated with this process. Notably, by eliminating the need to keep the teacher model in memory and utilizing precomputed data, training costs have been lowered. These changes are making large language models more accessible.