  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.
