tensor-train

The paper proposes ChainDoRA, a parameter-efficient fine-tuning method for large language models that combines Tensor-Train factorization with weight-decomposed low-rank adaptation, achieving a 90.62% reduction in trainable parameters and improved accuracy over LoRA and DoRA.