Recent advancements in Large Language Models (LLMs) have revolutionized artificial intelligence, yet developing an effective foundational LLM that balances high performance with computational efficiency remains challenging, especially for emerging research groups. To address this gap, we introduce Motif-2.6B, a 2.6-billion-parameter foundation model designed to democratize advanced LLM capabilities. Motif-2.6B incorporates several innovative architectural enhancements, including Differential Attention and PolyNorm activation functions, which improve long-context comprehension, reduce hallucination, and enhance in-context learning capabilities. We rigorously tested multiple novel architectural components through extensive experimentation to determine the optimal architecture for Motif-2.6B. Comprehensive evaluations demonstrate that Motif-2.6B consistently meets or exceeds the performance of similarly sized state-of-the-art models across diverse benchmarks, showcasing its effectiveness, scalability, and real-world applicability. Through detailed experiments and tailored techniques, Motif-2.6B significantly advances the landscape of efficient, scalable, and powerful foundational LLMs, offering valuable insights and a robust foundation for future research and deployment.
@article{arxiv.2508.09148,
title = {Motif 2.6B Technical Report},
author = {Junghwan Lim and Sungmin Lee and Dongseok Kim and Eunhwan Park and Hyunbyung Park and Junhyeok Lee and Wai Ting Cheung and Dahye Choi and Jaeheui Her and Jaeyeon Huh and Hanbin Jung and Changjin Kang and Beomgyu Kim and Jihwan Kim and Minjae Kim and Taehwan Kim and Youngrok Kim and Haesol Lee and Jeesoo Lee and Kungyu Lee and Dongpin Oh and Yeongjae Park and Bokki Ryu and Daewon Suh and Dongjoo Weon},
journal= {arXiv preprint arXiv:2508.09148},
year = {2025}
}