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cs.LG2025
SparseLoRA: Accelerating LLM Fine-Tuning with Contextual Sparsity
Samir Khaki, Xiuyu Li, Junxian Guo +7
Fine-tuning LLMs is both computationally and memory-intensive. While parameter-efficient fine-tuning methods, such as QLoRA and DoRA, reduce the number of trainable parameters and…
cs.LG2024
Prioritize Alignment in Dataset Distillation
Zekai Li, Ziyao Guo, Wangbo Zhao +8
Dataset Distillation aims to compress a large dataset into a significantly more compact, synthetic one without compromising the performance of the trained models. To achieve this,…
cs.LG2024
The Need for Speed: Pruning Transformers with One Recipe
Samir Khaki, Konstantinos N. Plataniotis
We introduce the ne-shot runing echnique for nterchangeable etworks () framework as a tool to increase t…