5 papers
Motif-Video 2B: Technical Report
Junghwan Lim, Wai Ting Cheung, Minsu Ha +25
Training strong video generation models usually requires massive datasets, large parameter counts, and substantial compute. In this work, we ask whether strong text-to-video qualit…
Motif-2-12.7B-Reasoning: A Practitioner's Guide to RL Training Recipes
Junghwan Lim, Sungmin Lee, Dongseok Kim +23
We introduce Motif-2-12.7B-Reasoning, a 12.7B parameter language model designed to bridge the gap between open-weight systems and proprietary frontier models in complex reasoning a…
Motif 2 12.7B technical report
Junghwan Lim, Sungmin Lee, Dongseok Kim +22
We introduce Motif-2-12.7B, a new open-weight foundation model that pushes the efficiency frontier of large language models by combining architectural innovation with system-level…
Grouped Differential Attention
Junghwan Lim, Sungmin Lee, Dongseok Kim +7
The self-attention mechanism, while foundational to modern Transformer architectures, suffers from a critical inefficiency: it frequently allocates substantial attention to redunda…
Motif 2.6B Technical Report
Junghwan Lim, Sungmin Lee, Dongseok Kim +22
Recent advancements in Large Language Models (LLMs) have revolutionized artificial intelligence, yet developing an effective foundational LLM that balances high performance with co…