arXiv AI
SplitLite: Low-Rank Residual Compression for Split Learning
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Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models
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PLoRA: Efficient Concurrent LoRA Training for Large Language Models
arXiv:2508. 02932v2 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) has gained popularity as a fine-tuning approach for Large Language Models (LLMs) due to its low resource requirements and good performance.
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arXiv:2607. 11940v1 Announce Type: cross Abstract: As the scale of large pre-trained models continues to grow, fine-tuning them under limited memory budgets has become increasingly challenging.
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Overcoming the Communication-Performance Tradeoff in LLM Pretraining
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SigmaScale: LLM Compression with SVD-based Low-Rank Decomposition and Learned Scaling Matrices
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