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cs.CL2025
Saten: Sparse Augmented Tensor Networks for Post-Training Compression of Large Language Models
Ryan Solgi, Kai Zhen, Rupak Vignesh Swaminathan +4
The efficient implementation of large language models (LLMs) is crucial for deployment on resource-constrained devices. Low-rank tensor compression techniques, such as tensor-train…
cs.CL2024
AdaZeta: Adaptive Zeroth-Order Tensor-Train Adaption for Memory-Efficient Large Language Models Fine-Tuning
Yifan Yang, Kai Zhen, Ershad Banijamal +2
Fine-tuning large language models (LLMs) has achieved remarkable performance across various natural language processing tasks, yet it demands more and more memory as model sizes ke…