Generative pre-trained transformer (GPT) models have revolutionized the field of natural language processing (NLP) with remarkable performance in various tasks and also extend their power to multimodal domains. Despite their success, large GPT models like GPT-4 face inherent limitations such as considerable size, high computational requirements, complex deployment processes, and closed development loops. These constraints restrict their widespread adoption and raise concerns regarding their responsible development and usage. The need for user-friendly, relatively small, and open-sourced alternative GPT models arises from the desire to overcome these limitations while retaining high performance. In this survey paper, we provide an examination of alternative open-sourced models of large GPTs, focusing on user-friendly and relatively small models that facilitate easier deployment and accessibility. Through this extensive survey, we aim to equip researchers, practitioners, and enthusiasts with a thorough understanding of user-friendly and relatively small open-sourced models of large GPTs, their current state, challenges, and future research directions, inspiring the development of more efficient, accessible, and versatile GPT models that cater to the broader scientific community and advance the field of general artificial intelligence. The source contents are continuously updating in https://github.com/GPT-Alternatives/gpt_alternatives.

翻译：生成式预训练Transformer（GPT）模型在自然语言处理领域取得了革命性进展，在各类任务中展现出卓越性能，并将能力拓展至多模态领域。尽管GPT-4等大型GPT模型成果显著，但其存在规模庞大、计算需求高、部署流程复杂及开发闭环等固有局限。这些制约因素阻碍了其广泛采用，并引发了关于负责任开发与使用的担忧。为突破上述瓶颈并维持高性能，亟需开发用户友好、相对轻量且开源替代的GPT模型。本综述论文系统审视了大型GPT的开源替代模型，聚焦于便于部署与访问的用户友好型轻量模型。通过广泛调研，旨在使研究人员、实践者及爱好者深入理解面向用户友好的相对轻量级大型GPT开源模型，厘清其当前发展现状、面临挑战及未来研究方向，从而激发更高效、易用且通用的GPT模型研发工作，服务更广泛的科学共同体，推动通用人工智能领域发展。相关资源持续更新于https://github.com/GPT-Alternatives/gpt_alternatives。