OpenBA: An Open-sourced 15B Bilingual Asymmetric seq2seq Model
  Pre-trained from Scratch
Authors: name: Juntao Li, name: Zecheng Tang, name: Yuyang Ding, name: Pinzheng Wang, name: Pei Guo, name: Wangjie You, name: Dan Qiao, name: Wenliang Chen, name: Guohong Fu, name: Qiaoming Zhu, name: Guodong Zhou, name: Min Zhang

Large language models (LLMs) with billions of parameters have demonstrated
outstanding performance on various natural language processing tasks. This
report presents OpenBA, an open-sourced 15B bilingual asymmetric seq2seq model,
to contribute an LLM variant to the Chinese-oriented open-source model
community. We enhance OpenBA with effective and efficient techniques as well as
adopt a three-stage training strategy to train the model from scratch. Our
solution can also achieve very competitive performance with only 380B tokens,
which is better than LLaMA-70B on the BELEBELE benchmark, BLOOM-176B on the
MMLU benchmark, GLM-130B on the C-Eval (hard) benchmark. This report provides
the main details to pre-train an analogous model, including pre-training data
processing, Bilingual Flan data collection, the empirical observations that
inspire our model architecture design, training objectives of different stages,
and other enhancement techniques. We have refactored our code to follow the
design principles of the Huggingface Transformers Library, making it more
convenient for developers to use, and released checkpoints of different
training stages at https://huggingface.co/openBA. More details of our project
are available at https://github.com/OpenNLG/openBA.git.