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cs.LG2025
MoE-Compression: How the Compression Error of Experts Affects the Inference Accuracy of MoE Model?
Songkai Ma, Zhaorui Zhang, Sheng Di +4
With the widespread application of Mixture of Experts (MoE) reasoning models in the field of LLM learning, efficiently serving MoE models under limited GPU memory constraints has e…
cs.LG2025
CLLoRA: An Approach to Measure the Effects of the Context Length for LLM Fine-Tuning
Ping Zhang, Zhaorui Zhang, Sheng Di +2
Large language model fine-tuning has been identified as an efficient approach to applying the pre-trained Large language models to other domains. To guarantee data privacy for diff…
cs.LG2024★ 1 cited
FedFa: A Fully Asynchronous Training Paradigm for Federated Learning
Haotian Xu, Zhaorui Zhang, Sheng Di +3
Federated learning has been identified as an efficient decentralized training paradigm for scaling the machine learning model training on a large number of devices while guaranteei…