This article introduces BiTA, an innovative technology that accelerates the generation of large language models (LLMs) through bidirectional tuning and tree decoding technology. It adopts a universal architecture and pluggable design, and is especially suitable for real-time application scenarios such as chatbots. BiTA's efficiency is reflected in its achievement of 2.1× to 3.3× acceleration effects in a wide range of generation task tests, and its adjustable hint design makes it easy to apply to various transformer-based LLMs.
In recent years, BiTA has accelerated the generation of large language models (LLMs) through technological innovations in bidirectional tuning and tree decoding. Adopting a universal architecture and pluggable design, it is especially suitable for real-time applications such as chatbots. Through two-way tuning and SAR draft verification, lossless acceleration of the autoregressive language model is achieved. The study found that BiTA achieved impressive speedups of 2.1× to 3.3× when tested on a wide range of generation tasks. Its adjustable hint design makes it a plug-and-play method that can be used with any publicly accessible transformer-based LLMs.The emergence of BiTA technology has brought significant performance improvements to the application of large language models. Its efficiency and ease of use make it have broad application prospects in the future. Further research can explore the performance of BiTA in more types of LLMs and application scenarios, and how to further optimize its efficiency and scalability.