Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
June 11, 2024Open Access

ALPS: Improved Optimization for Highly Sparse One-Shot Pruning for Large Language Models

View Full Paper
Ask AI
Bookmark
Share

Authors

MXMeng XiangChina State Construction Engineering (China)KBKayhan BehdinLinkedIn (United States)HWHaoyue WangPeople's Liberation Army 401 Hospital

Discussion

Loading...

Member takes

Implication

Key Points

Key points are not available for this paper at this time.

Cite This Study

Xiang et al. (2024) studied this question.

synapsesocial.com/papers/68e65555b6db6435875e49fchttps://doi.org/10.48550/arxiv.2406.07831
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Bypass Back-propagation: Optimization-based Structural Pruning for Large Language Models via Policy Gradient2024
  2. 2SparseLLM: Towards Global Pruning for Pre-trained Language Models2024
  3. 3Dual-Assessment Driven Pruning: Iterative Optimizing Layer-wise Sparsity for Large Language Model2024 · 5 citations
  4. 4LLM-Barber: Block-Aware Rebuilder for Sparsity Mask in One-Shot for Large Language Models2024
  5. 5One-Shot Sensitivity-Aware Mixed Sparsity Pruning for Large Language Models2024 · 26 citations