XGen-7B Technical Report
Authors: name: Erik Nijkamp, name: Tian Xie, name: Hiroaki Hayashi, name: Bo Pang, name: Congying Xia, name: Chen Xing, name: Jesse Vig, name: Semih Yavuz, name: Philippe Laban, name: Ben Krause, name: Senthil Purushwalkam, name: Tong Niu, name: Wojciech Kryściński, name: Lidiya Murakhovs'ka, name: Prafulla Kumar Choubey, name: Alex Fabbri, name: Ye Liu, name: Rui Meng, name: Lifu Tu, name: Meghana Bhat, name: Chien-Sheng Wu, name: Silvio Savarese, name: Yingbo Zhou, name: Shafiq Joty, name: Caiming Xiong

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.