The research focuses on optimizing algorithms for training large language models (LLMs) crucial for natural language processing and AI applications. The Adam optimizer, commonly used for LLM training, faces high memory demands, making training expensive and less accessible. To address this, researchers introduced Adam-mini, reducing memory usage by 45-50% while maintaining or improving performance. By partitioning model parameters based on Hessian structure, Adam-mini simplifies learning rate assignment, leading to faster training and reduced memory footprint. The optimizer outperformed AdamW in various tasks, showcasing its efficiency across different language models. Overall, Adam-mini offers a valuable solution for researchers working with large-scale language models, enhancing training feasibility and encouraging broader participation. The innovative approach of Adam-mini significantly improves memory efficiency and training speed, making it a promising tool for optimizing LLM training processes.
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Adam-mini: Memory-efficient optimizer for large language model training. #Efficiency
