4 citations · 4 across the 4 of their papers we have counts for
4 papers
Understanding and Alleviating Memory Consumption in RLHF for LLMs
Jin Zhou, Hanmei Yang, Steven +4
Fine-tuning with Reinforcement Learning with Human Feedback (RLHF) is essential for aligning large language models (LLMs). However, RLHF often encounters significant memory challen…
AdapMTL: Adaptive Pruning Framework for Multitask Learning Model
Mingcan Xiang, Steven Jiaxun Tang, Qizheng Yang +2
In the domain of multimedia and multimodal processing, the efficient handling of diverse data streams such as images, video, and sensor data is paramount. Model compression and mul…
ProTrain: Efficient LLM Training via Memory-Aware Techniques
Hanmei Yang, Jin Zhou, Yao Fu +4
Memory pressure has emerged as a dominant constraint in scaling the training of large language models (LLMs), particularly in resource-constrained environments. While modern framew…
NumaPerf: Predictive and Full NUMA Profiling
Xin Zhao, Jin Zhou, Hui Guan +3
Parallel applications are extremely challenging to achieve the optimal performance on the NUMA architecture, which necessitates the assistance of profiling tools. However, existing…