Xgen-7b Technical Report

Nijkamp Erik, Xie Tian, Hayashi Hiroaki, Pang Bo, Xia Congying, Xing Chen, Vig Jesse, Yavuz Semih, Laban Philippe, Krause Ben, Purushwalkam Senthil, Niu Tong, Kryściński Wojciech, Murakhovs'ka Lidiya, Choubey Prafulla Kumar, Fabbri Alex, Liu Ye, Meng Rui, Tu Lifu, Bhat Meghana, Wu Chien-sheng, Savarese Silvio, Zhou Yingbo, Joty Shafiq, Xiong Caiming. Arxiv 2023

[Paper]    
Applications Reinforcement Learning

Large Language Models (LLMs) have become ubiquitous across various domains, transforming the way we interact with information and conduct research. However, most high-performing LLMs remain confined behind proprietary walls, hindering scientific progress. Most open-source LLMs, on the other hand, are limited in their ability to support longer sequence lengths, which is a key requirement for many tasks that require inference over an input context. To address this, we have trained XGen, a series of 7B parameter models on up to 8K sequence length for up to 1.5T tokens. We have also finetuned the XGen models on public-domain instructional data, creating their instruction-tuned counterparts (XGen-Inst). We open-source our models for both research advancements and commercial applications. Our evaluation on standard benchmarks shows that XGen models achieve comparable or better results when compared with state-of-the-art open-source LLMs. Our targeted evaluation on long sequence modeling tasks shows the benefits of our 8K-sequence models over 2K-sequence open-source LLMs.

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