LLM-Shearing and LLaMA-Pruning
These are competitors—both implement structured pruning approaches to reduce LLaMA model size and latency, with LLM-Shearing being the more established academic solution (ICLR 2024 publication, 10x more stars) while LLaMA-Pruning offers an alternative implementation of similar structural pruning techniques.
Maintenance
0/25
Adoption
10/25
Maturity
16/25
Community
16/25
Maintenance
0/25
Adoption
8/25
Maturity
16/25
Community
9/25
Stars: 642
Forks: 57
Downloads: —
Commits (30d): 0
Language: Python
License: MIT
Stars: 54
Forks: 4
Downloads: —
Commits (30d): 0
Language: Python
License: GPL-3.0
Stale 6m
No Package
No Dependents
Archived
Stale 6m
No Package
No Dependents
About LLM-Shearing
princeton-nlp/LLM-Shearing
[ICLR 2024] Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning
About LLaMA-Pruning
horseee/LLaMA-Pruning
Structural Pruning for LLaMA
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