  Large language models (LLMs) have demonstrated exceptional performance in
various NLP applications. However, the majority of existing open-source LLMs
are pre-trained primarily on English data and little part of other languages.
This deficiency in multilingual training data results in suboptimal performance
when applied to languages with fewer available resources. Furthermore,
enhancing the performance of LLMs on low-resource languages by full-parameter
fine-tuning with additional data requires substantial computational resources,
posing computational barriers for research organizations and individual
researchers. Consequently, several techniques such as parameter-efficient
tuning and advanced embedding initialization have been proposed to address
these challenges. In this work, we combine them to facilitate cross-lingual
transfer on English-dominated open-source LLM. To effectively enhance the
model's proficiency in Traditional Chinese, we conduct secondary pre-training
on Llama 2 7B with Traditional Chinese data by leveraging QLoRA and our
proposed zip-tie embedding initialization. The resulting model called Bailong,
which stands for Bilingual trAnsfer learnIng based on qLOra and zip-tie
embeddiNG. We present Bailong-instruct 7B, a fine-tuned version of Bailong 7B
optimized for multi-turn dialogue scenarios. Recognizing the inadequacy of
benchmark datasets in Traditional Chinese, we further introduce Bailong-bench
to assess the alignment of models with human preferences and the capability to
follow instructions in both Traditional Chinese and English tasks. In our
evaluation, Bailong-instruct 7B exhibits competitive performance on
Bailong-bench and other benchmark datasets when compared to other open-source
models of similar or even larger parameter sizes. Bailong-instruct 7B and
Bailong-bench are publicly available with the aim of empowering the community
to build upon our efforts.
