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| Format: | Preprint |
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2025
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| Online-Zugang: | https://arxiv.org/abs/2508.09148 |
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| _version_ | 1866912535488233472 |
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| author | Lim, Junghwan Lee, Sungmin Kim, Dongseok Park, Eunhwan Park, Hyunbyung Lee, Junhyeok Cheung, Wai Ting Choi, Dahye Her, Jaeheui Huh, Jaeyeon Jung, Hanbin Kang, Changjin Kim, Beomgyu Kim, Jihwan Kim, Minjae Kim, Taehwan Kim, Youngrok Lee, Haesol Lee, Jeesoo Lee, Kungyu Oh, Dongpin Park, Yeongjae Ryu, Bokki Suh, Daewon Weon, Dongjoo |
| author_facet | Lim, Junghwan Lee, Sungmin Kim, Dongseok Park, Eunhwan Park, Hyunbyung Lee, Junhyeok Cheung, Wai Ting Choi, Dahye Her, Jaeheui Huh, Jaeyeon Jung, Hanbin Kang, Changjin Kim, Beomgyu Kim, Jihwan Kim, Minjae Kim, Taehwan Kim, Youngrok Lee, Haesol Lee, Jeesoo Lee, Kungyu Oh, Dongpin Park, Yeongjae Ryu, Bokki Suh, Daewon Weon, Dongjoo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_09148 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Motif 2.6B Technical Report Lim, Junghwan Lee, Sungmin Kim, Dongseok Park, Eunhwan Park, Hyunbyung Lee, Junhyeok Cheung, Wai Ting Choi, Dahye Her, Jaeheui Huh, Jaeyeon Jung, Hanbin Kang, Changjin Kim, Beomgyu Kim, Jihwan Kim, Minjae Kim, Taehwan Kim, Youngrok Lee, Haesol Lee, Jeesoo Lee, Kungyu Oh, Dongpin Park, Yeongjae Ryu, Bokki Suh, Daewon Weon, Dongjoo Machine Learning Artificial Intelligence 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. |
| title | Motif 2.6B Technical Report |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2508.09148 |