2 citations · 3 across the 8 of their papers we have counts for
5 papers · 1 filter
Discovering Decoupled Functional Modules in Large Language Models
Yanke Yu, Jin Li, Ying Sun +3
Understanding the internal functional organization of Large Language Models (LLMs) is crucial for improving their trustworthiness and performance. However, how LLMs organize differ…
Adacc: An Adaptive Framework Unifying Compression and Activation Recomputation for LLM Training
Ping Chen, Zhuohong Deng, Ping Li +6
Training large language models (LLMs) is often constrained by GPU memory limitations. To alleviate memory pressure, activation recomputation and data compression have been proposed…
FASP: Fast and Accurate Structured Pruning of Large Language Models
Hanyu Hu, Pengxiang Zhao, Ping Li +3
The rapid increase in the size of large language models (LLMs) has significantly escalated their computational and memory demands, posing challenges for efficient deployment, espec…
A Convex-optimization-based Layer-wise Post-training Pruner for Large Language Models
Pengxiang Zhao, Hanyu Hu, Ping Li +3
Pruning is a critical strategy for compressing trained large language models (LLMs), aiming at substantial memory conservation and computational acceleration without compromising p…
Adapprox: Adaptive Approximation in Adam Optimization via Randomized Low-Rank Matrices
Pengxiang Zhao, Ping Li, Yingjie Gu +4
As deep learning models exponentially increase in size, optimizers such as Adam encounter significant memory consumption challenges due to the storage of first and second moment da…