Extending Llama-3's Context Ten-Fold Overnight
Authors: name: Peitian Zhang, name: Ninglu Shao, name: Zheng Liu, name: Shitao Xiao, name: Hongjin Qian, name: Qiwei Ye, name: Zhicheng Dou

We extend the context length of Llama-3-8B-Instruct from 8K to 80K via QLoRA
fine-tuning. The entire training cycle is super efficient, which takes 8 hours
on one 8xA800 (80G) GPU machine. The resulted model exhibits superior
performances across a broad range of evaluation tasks, such as NIHS, topic
retrieval, and long-context language understanding; meanwhile, it also well
preserves the original capability over short contexts. The dramatic context
extension is mainly attributed to merely 3.5K synthetic training samples
generated by GPT-4 , which indicates the LLMs' inherent (yet largely
underestimated) potential to extend its original context length. In fact, the
context length could be extended far beyond 80K with more computation
resources. Therefore, the team will publicly release the entire resources
(including data, model, data generation pipeline, training code) so as to
facilitate the future research from the community:
https://github.com/FlagOpen/FlagEmbedding.