This report introduces \texttt{EEVE-Korean-v1.0}, a Korean adaptation of
large language models that exhibit remarkable capabilities across English and
Korean text understanding. Building on recent highly capable but
English-centric LLMs, such as SOLAR-10.7B and Phi-2, where non-English texts
are inefficiently processed with English-centric tokenizers, we present an
efficient and effective vocabulary expansion (EEVE) method, which encompasses
parameter freezing and subword initialization. In contrast to previous efforts
that believe new embeddings require trillions of training tokens, we show that
our method can significantly boost non-English proficiency within just 2
billion tokens. Surpassing most instruction-tuned LLMs on the Open Ko-LLM
Leaderboard, as of January 2024, our model \texttt{EEVE-Korean-10.8B-v1.0}
ranks as the leading Korean pre-trained model in the open-source community,
according to Hugging Face's leaderboard. We open-source our models on
Huggingface to empower the open research community in various languages.

介绍了 EEVE-Korean-v1.0，这是一个在英语和韩语文本理解方面表现出非凡能力的韩语适应版本的大型语言模型。通过引入一个高效有效的词汇扩展（EEVE）方法，该模型能够显着提升非英语能力。EEVE-Korean-10.8B-v1.0 在 Open Ko-LLM 排行榜上超过了大多数指令调整的 LLMs，并成为 2024 年 1 月开源社区中领先的韩语预训练模型。