We introduce GECKO, a bilingual large language model (LLM) optimized for
Korean and English, along with programming languages. GECKO is pretrained on
the balanced, high-quality corpus of Korean and English employing LLaMA
architecture. In this report, we share the experiences of several efforts to
build a better data pipeline for the corpus and to train our model. GECKO shows
great efficiency in token generations for both Korean and English, despite its
small size of vocabulary. We measure the performance on the representative
benchmarks in terms of Korean, English and Code, and it exhibits great
performance on KMMLU (Korean MMLU) and modest performance in English and Code,
even with its smaller number of trained tokens compared to English-focused
LLMs. GECKO is available to the open-source community under a permissive
license. We hope our work offers a research baseline and practical insights for
Korean LLM research. The model can be found at:
this https URL

GECKO 是一种针对韩语和英语以及编程语言进行优化的双语大型语言模型 (LLM)，通过 LLaMA 架构在平衡、高质量的韩语和英语语料库上进行预训练。该模型在韩语和英语的令牌生成效率上表现出色，尽管其词汇量较小。希望此研究能为韩语 LLM 研究提供一个基准和实际见解。