11 citations · 11 across the 1 of their papers we have counts for
2 papers
cs.CL2026★ 11 cited
LoRA-FA: Efficient and Effective Low Rank Representation Fine-tuning
Longteng Zhang, Lin Zhang, Shaohuai Shi +2
Fine-tuning large language models (LLMs) is crucial for improving their performance on downstream tasks, but full-parameter fine-tuning (Full-FT) is computationally expensive and m…
cs.LG2025
FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models
Xinglin Pan, Wenxiang Lin, Lin Zhang +5
Recent large language models (LLMs) have tended to leverage sparsity to reduce computations, employing the sparsely activated mixture-of-experts (MoE) technique. MoE introduces fou…