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.

我们训练了 XGen，这是一系列包含 70 亿参数的模型，可支持长度达 8K 的序列，同时通过公共领域的指导数据进行了微调，与最先进的开源 LLMs 相比，在标准基准测试和长序列建模任务中表现出可比或更好的结果。