Unverified50% confidenceFactExact time
In the original paper by Hu et al. at ICLR 2022, it was proposed that freezing base model weights and representing each update matrix with two low-rank matrices can drastically reduce trainable parameters while achieving quality close to full fine-tuning on benchmark tasks.
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10/11/2026
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VerifiedDuring LoRA fine-tuning, the original model parameters are frozen and only the inserted low-rank matrices are updated, significantly reducing VRAM requirements and training costs.76% similarVerifiedLoRA将对权重矩阵的全量更新分解为两个低秩矩阵的乘积，训练时只更新这两个小矩阵，原始权重保持冻结75% similarVerifiedThe core idea of PEFT is to not update all parameters, but instead introduce a small number of trainable adapter parameters (such as LoRA's low-rank matrix decomposition), reducing VRAM and computation requirements while retaining performance close to full fine-tuning.72% similarUnverifiedLoRA leverages the low-rank approximation principle of SVD to decompose the incremental updates of large weight matrices into the product of two low-rank matrices, thereby compressing the number of trainable parameters from billions to the millions range.71% similarUnverifiedIf the dataset is small while the model has too many parameters, the model may memorize the training data prematurely and degenerate; this can be mitigated by adding more data, applying dropout regularization, or reducing model size.66% similar
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