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Introducing PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models
Overview
Fine-tuning large language models (LLMs) can enhance task performance and adherence to instructions, but it often comes with high GPU memory costs. To address this, parameter-efficient fine-tuning (PEFT) methods like PiSSA have been introduced, offering practical solutions to reduce memory usage without sacrificing performance.
Practical Solutions and Value
PiSSA optimizes a reduced parameter space by utilizing Singular Value Decomposition (SVD) to factorize matrices within the model. This method initializes adapters with principal components, leading to superior fine-tuning performance compared to other methods like LoRA. PiSSA offers an efficient and easy-to-use initialization method, demonstrating robust superiority under similar trainable parameter configurations.
Benefits
By efficiently identifying and fine-tuning the model’s principal components, PiSSA offers a promising approach to PEFT. It aligns closely with training data, converges swiftly, and balances initialization speed and performance through the use of the Fast SVD technique.
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