1 citations · 2 across the 14 of their papers we have counts for
4 papers · 1 filter
Loquetier: A Virtualized Multi-LoRA Framework for Unified LLM Fine-tuning and Serving
Yuchen Zhang, Hanyue Du, Chun Cao +1
Low-Rank Adaptation (LoRA) has become a widely adopted parameter-efficient fine-tuning (PEFT) technique for adapting large language models (LLMs) to downstream tasks. While prior w…
Toward Efficient Influence Function: Dropout as a Compression Tool
Yuchen Zhang, Mohammad Mohammadi Amiri
Assessing the impact the training data on machine learning models is crucial for understanding the behavior of the model, enhancing the transparency, and selecting training data. I…
Echo: Decoupling Inference and Training for Large-Scale RL Alignment on Heterogeneous Swarms
Jie Xiao, Changyuan Fan, Qingnan Ren +6
Modern RL-based post-training for large language models (LLMs) co-locate trajectory sampling and policy optimisation on the same GPU cluster, forcing the system to switch between i…
Adversarial Curriculum Graph-Free Knowledge Distillation for Graph Neural Networks
Yuang Jia, Xiaojuan Shan, Jun Xia +5
Data-free Knowledge Distillation (DFKD) is a method that constructs pseudo-samples using a generator without real data, and transfers knowledge from a teacher model to a student by…