arXiv (All)
open access
Diet Your LLM: Dimension-wise Global Pruning of LLMs via Merging Task-specific Importance Score
Large language models (LLMs) have demonstrated remarkable capabilities, but their massive scale poses significant challenges for practical deployment. Structured pruning offers a promising solution by removing entire dimensions or layers, yet existing methods face critical trade-offs: task-agnostic
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- SinkPruner: Sink-Free Visual Token Pruning for Multimodal Large Language Models#820996
- Unlocking Structured Pruning for Reasoning Language Models via Attention-Preserving MLP Trimming#1344944
- Relative Kinetic Utility: Calibrating Cross-Layer Credit for Global Structured LLM Pruning#1141534
- Large Language Models: A Structured Taxonomy and Review of Challenges, Limitations, Solutions, and Future Directions#285464
- DIPrune: Task-Aware Token Pruning with Dual Importance for Efficient Multimodal Language Models#1218076
Record · ID 1389234
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive