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Generative pre-trained transformer (GPT) models have revolutionized the fieldof natural language processing (NLP) with remarkable performance in varioustasks and also extend their power to multimodal domains. Despite their success,large GPT models like GPT-4 face inherent limitations such as considerablesize, high computational requirements, complex deployment processes, and closeddevelopment loops. These constraints restrict their widespread adoption andraise concerns regarding their responsible development and usage. The need foruser-friendly, relatively small, and open-sourced alternative GPT models arisesfrom the desire to overcome these limitations while retaining high performance.In this survey paper, we provide an examination of alternative open-sourcedmodels of large GPTs, focusing on user-friendly and relatively small modelsthat facilitate easier deployment and accessibility. Through this extensivesurvey, we aim to equip researchers, practitioners, and enthusiasts with athorough understanding of user-friendly and relatively small open-sourcedmodels of large GPTs, their current state, challenges, and future researchdirections, inspiring the development of more efficient, accessible, andversatile GPT models that cater to the broader scientific community and advancethe field of general artificial intelligence. The source contents arecontinuously updating in https://github.com/GPT-Alternatives/gpt_alternatives.

Examining User-Friendly and Open-Sourced Large GPT Models: A Survey on Language, Multimodal, and Scientific GPT Models