12 citations · 29 across the 17 of their papers we have counts for
7 papers · 1 filter
Task Scheduling for Efficient Inference of Large Language Models on Single Moderate GPU Systems
Wenxiang Lin, Xinglin Pan, Shaohuai Shi +2
Large language models~(LLMs) are known for their high demand on computing resources and memory due to their substantial model size, which leads to inefficient inference on moderate…
Should We Really Edit Language Models? On the Evaluation of Edited Language Models
Qi Li, Xiang Liu, Zhenheng Tang +4
Model editing has become an increasingly popular alternative for efficiently updating knowledge within language models. Current methods mainly focus on reliability, generalization,…
FusionLLM: A Decentralized LLM Training System on Geo-distributed GPUs with Adaptive Compression
Zhenheng Tang, Xueze Kang, Yiming Yin +11
To alleviate hardware scarcity in training large deep neural networks (DNNs), particularly large language models (LLMs), we present FusionLLM, a decentralized training system desig…
Multi-Task Domain Adaptation for Language Grounding with 3D Objects
Penglei Sun, Yaoxian Song, Xinglin Pan +6
The existing works on object-level language grounding with 3D objects mostly focus on improving performance by utilizing the off-the-shelf pre-trained models to capture features, s…
Parm: Efficient Training of Large Sparsely-Activated Models with Dedicated Schedules
Xinglin Pan, Wenxiang Lin, Shaohuai Shi +3
Sparsely-activated Mixture-of-Expert (MoE) layers have found practical applications in enlarging the model size of large-scale foundation models, with only a sub-linear increase in…
Pruner-Zero: Evolving Symbolic Pruning Metric from scratch for Large Language Models
Peijie Dong, Lujun Li, Zhenheng Tang +4
Despite the remarkable capabilities, Large Language Models (LLMs) face deployment challenges due to their extensive size. Pruning methods drop a subset of weights to accelerate, bu…
ParZC: Parametric Zero-Cost Proxies for Efficient NAS
Peijie Dong, Lujun Li, Xinglin Pan +4
Recent advancements in Zero-shot Neural Architecture Search (NAS) highlight the efficacy of zero-cost proxies in various NAS benchmarks. Several studies propose the automated desig…