The recent breakthroughs in Large Language Models (LLMs) have mostly focused
on languages with easily available and sufficient resources, such as English.
However, there remains a significant gap for languages that lack sufficient
linguistic resources in the public domain. Our work introduces Komodo-7B,
7-billion-parameter Large Language Models designed to address this gap by
seamlessly operating across Indonesian, English, and 11 regional languages in
Indonesia. Komodo-7B is a family of LLMs that consist of Komodo-7B-Base and
Komodo-7B-Instruct. Komodo-7B-Instruct stands out by achieving state-of-the-art
performance in various tasks and languages, outperforming the benchmarks set by
OpenAI's GPT-3.5, Cohere's Aya-101, Llama-2-Chat-13B,
Mixtral-8x7B-Instruct-v0.1, Gemma-7B-it , and many more. This model not only
demonstrates superior performance in both language-specific and overall
assessments but also highlights its capability to excel in linguistic
diversity. Our commitment to advancing language models extends beyond
well-resourced languages, aiming to bridge the gap for those with limited
linguistic assets. Additionally, Komodo-7B-Instruct's better cross-language
understanding contributes to addressing educational disparities in Indonesia,
offering direct translations from English to 11 regional languages, a
significant improvement compared to existing language translation services.
Komodo-7B represents a crucial step towards inclusivity and effectiveness in
language models, providing to the linguistic needs of diverse communities.

Komodo-7B 是一种 70 亿参数的大型语言模型，能无缝运行于印尼、英语和印尼的 11 种地方语言，比 OpenAI 的 GPT-3.5、Cohere 的 Aya-101、Llama-2-Chat-13B、Mixtral-8x7B-Instruct-v0.1、Gemma-7B-it 等模型表现更好，在语言特定和总体评估中都表现出卓越的性能，还能进行英语到 11 种地方语言的直译，对减少印尼的教育差距具有重要意义。