Mi:dm 2.0 Korea-centric Bilingual Language Models
Donghoon Shin, Sejung Lee, Soonmin Bae, Hwijung Ryu, Changwon Ok, Hoyoun Jung, Hyesung Ji, Jeehyun Lim, Jehoon Lee, Ji-Eun Han, Jisoo Baik, Mihyeon Kim, Riwoo Chung, Seongmin Lee, Wonjae Park, Yoonseok Heo, Youngkyung Seo, Seyoun Won, Boeun Kim, Cheolhun Heo, Eunkyeong Lee, Honghee Lee, Hyeongju Ju, Hyeontae Seo, Jeongyong Shim, Jisoo Lee, Junseok Koh, Junwoo Kim, Minho Lee, Minji Kang, Minju Kim, Sangha Nam, Seongheum Park, Taehyeong Kim, Euijai Ahn, Hong Seok Jeung, Jisu Shin, Jiyeon Kim, Seonyeong Song, Seung Hyun Kong, Sukjin Hong, Taeyang Yun, Yu-Seon Kim, A-Hyun Lee, Chae-Jeong Lee, Hye-Won Yu, Ji-Hyun Ahn, Song-Yeon Kim, Sun-Woo Jung, Eunju Kim, Eunji Ha, Jinwoo Baek, Yun-ji Lee, Wanjin Park, Jeong Yeop Kim, Eun Mi Kim, Hyoung Jun Park, Jung Won Yoon, Min Sung Noh, Myung Gyo Oh, Wongyoung Lee, Yun Jin Park, Young S. Kwon, Hyun Keun Kim, Jieun Lee, YeoJoo Park
TL;DR
Mi:dm 2.0 presents a Korea-centric bilingual LLM designed to embed Korean cultural values and reasoning into language modeling. It introduces two configurations, Base (11.5B) and Mini (2.3B), achieved through Depth Up-Scaling and multi-stage pruning/distillation, plus long-context extensions to 32K tokens. A rigorous data pipeline—with a multidimensional taxonomy, heavy quality filtering, and targeted synthetic data—drives domain-balanced pre-training, while post-training aligns the model via supervised fine-tuning and preference optimization to enhance instruction following, reasoning, RAG, tool use, safety, and long-context handling. Comprehensive evaluations across English and Korean benchmarks, human judgments, and RAI metrics demonstrate strong Korean-language and cultural capabilities, safety, and robustness, with the model released under MIT license to accelerate Korean AI development and adoption.
Abstract
We introduce Mi:dm 2.0, a bilingual large language model (LLM) specifically engineered to advance Korea-centric AI. This model goes beyond Korean text processing by integrating the values, reasoning patterns, and commonsense knowledge inherent to Korean society, enabling nuanced understanding of cultural contexts, emotional subtleties, and real-world scenarios to generate reliable and culturally appropriate responses. To address limitations of existing LLMs, often caused by insufficient or low-quality Korean data and lack of cultural alignment, Mi:dm 2.0 emphasizes robust data quality through a comprehensive pipeline that includes proprietary data cleansing, high-quality synthetic data generation, strategic data mixing with curriculum learning, and a custom Korean-optimized tokenizer to improve efficiency and coverage. To realize this vision, we offer two complementary configurations: Mi:dm 2.0 Base (11.5B parameters), built with a depth-up scaling strategy for general-purpose use, and Mi:dm 2.0 Mini (2.3B parameters), optimized for resource-constrained environments and specialized tasks. Mi:dm 2.0 achieves state-of-the-art performance on Korean-specific benchmarks, with top-tier zero-shot results on KMMLU and strong internal evaluation results across language, humanities, and social science tasks. The Mi:dm 2.0 lineup is released under the MIT license to support extensive research and commercial use. By offering accessible and high-performance Korea-centric LLMs, KT aims to accelerate AI adoption across Korean industries, public services, and education, strengthen the Korean AI developer community, and lay the groundwork for the broader vision of K-intelligence. Our models are available at https://huggingface.co/K-intelligence. For technical inquiries, please contact midm-llm@kt.com.