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This research aims to build generative language models specialized for thelegal domain. The manuscript presents the development of LexGPT models based onGPT-J models and pre-trained with Pile of Law. The foundation model built inthis manuscript is the initial step for the development of future applicationsin the legal domain, such as further training with reinforcement learning fromhuman feedback. Another objective of this manuscript is to assist legalprofessionals in utilizing language models through the ``No Code'' approach. Byfine-tuning models with specialized data and without modifying any source code,legal professionals can create custom language models for downstream tasks withminimum effort and technical knowledge. The downstream task in this manuscriptis to turn a LexGPT model into a classifier, although the performance isnotably lower than the state-of-the-art result. How to enhance downstream taskperformance without modifying the model or its source code is a research topicfor future exploration.

LexGPT 0.1: pre-trained GPT-J models with Pile of Law