EE-Tuning: An Economical yet Scalable Solution for Tuning Early-Exit
  Large Language Models
Xuchen Pan Yanxi Chen Yaliang Li Bolin Ding Jingren Zhou
This work introduces EE-Tuning, a lightweight and economical solution to
training/tuning early-exit large language models (LLMs). In contrast to the
common approach of full-parameter pre-training, EE-Tuning augments any
pre-trained (and possibly fine-tuned) standard LLM with additional early-exit
layers that are tuned in a parameter-efficient manner, which requires
significantly less computational resources and training data. Our
implementation of EE-Tuning achieves outstanding training efficiency via
extensive performance optimizations, as well as scalability due to its full
compatibility with 3D parallelism. Results of systematic experiments validate
the efficacy of EE-Tuning, confirming that effective early-exit LLM inference
can be achieved with a limited training budget. In hope of making early-exit
LLMs accessible to the community, we release the source code of our
implementation of EE-Tuning at https://github.com/pan-x-c/EE-LLM.