Practical Solutions for Large Language Model Training
Optimizing Algorithms for Training Large Language Models
The research focuses on optimizing algorithms for training large language models (LLMs), essential for natural language processing and artificial intelligence applications. The high memory demand of optimization algorithms, such as the Adam optimizer, poses a significant challenge, making training large models expensive and less accessible.
Introducing Adam-mini: A Memory-Efficient Optimizer
Adam-mini is an optimizer designed to achieve similar or better performance than Adam while reducing memory usage by 45% to 50%. By partitioning model parameters into blocks and assigning a single high-quality learning rate to each block, Adam-mini significantly reduces the memory footprint and simplifies the learning rate assignment process.
Improving Efficiency and Performance
Adam-mini achieved a throughput of 5572.19 tokens per second during pre-training, representing a 49.6% increase compared to AdamW. It also resulted in a 33% reduction in wall-clock time for processing the same number of tokens. Additionally, it consistently outperformed AdamW in supervised fine-tuning and reinforcement learning tasks, achieving higher evaluation scores and faster convergence.
Value of Adam-mini
The Adam-mini optimizer addresses the significant memory inefficiencies of traditional optimization methods like Adam, resulting in substantial memory savings and improved training efficiency. By reducing the memory footprint by up to 50% and increasing throughput by nearly 50%, Adam-mini enhances the feasibility of training large models and encourages broader participation from researchers with limited GPU resources.
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