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DoRA: Weight-Decomposed Low-Rank Adaptation Authors: Shih-Yang Liu, Chien-Yi Wang, Hongxu Yin, Pavlo Molchanov, Yu-Chiang Frank Wang, Kwang-Ting Cheng, Min-Hung Chen Source and references: https://t.co/gxSZAsJSQU Introduction This paper introduces a novel parameter-efficient… https://t.co/dQA9BwdDAA
Weight-Decomposed Low-Rank Adaptation (DoRA) from @nvidia. Quite a groundbreaking research for low-GPU LLM fine-tuning. Consistently outperforms LoRA across a wide variety of LLM and vision language model (VLM) 🤯 Begins by decomposing the pre-trained weight into its magnitude… https://t.co/ZVxp6VmY28
I finally read up on LoRA last night. LoRA can reduce finetuning params by 10,000 times. High-level implementation: • we freeze original LLM weights • we create small, low-rank matrices • we only train small matrices • we adjust LLM output w/ small matrices Instead of… https://t.co/YXvQKQEXi9
Researchers have introduced new techniques in the field of machine learning, such as LoRA+ for efficient low-rank adaptation of large models, OLoRA for faster convergence of LLM training, LoRA-GA for low-rank adaptation with gradient approximation, and DoRA for weight-decomposed low-rank adaptation. These methods aim to reduce the number of trainable parameters and GPU memory footprint while improving model performance.