  Inspired by Federated Learning, in this paper, we propose personal large
models that are distilled from traditional large language models but more
adaptive to local users' personal information such as education background and
hobbies. We classify the large language models into three levels: the personal
level, expert level and traditional level. The personal level models are
adaptive to users' personal information. They encrypt the users' input and
protect their privacy. The expert level models focus on merging specific
knowledge such as finance, IT and art. The traditional models focus on the
universal knowledge discovery and upgrading the expert models. In such
classifications, the personal models directly interact with the user. For the
whole system, the personal models have users' (encrypted) personal information.
Moreover, such models must be small enough to be performed on personal
computers or mobile devices. Finally, they also have to response in real-time
for better user experience and produce high quality results. The proposed
personal large models can be applied in a wide range of applications such as
language and vision tasks.
