LoftQ: Reimagining LLM fine-tuning with smarter initialization

This research paper was presented at the 12^th International Conference on Learning Representations (opens in new tab) (ICLR 2024), the premier conference dedicated to the advancement of deep learning.

Large language models (LLMs) use extensive datasets and advanced algorithms to generate nuanced, context-sensitive content. However, their development requires substantial computational resources. To address this, we developed LoftQ, an innovative technique that streamlines the fine-tuning process—which is used to

 

 

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