LLM-Pruner and LLM-Shearing

These are **competitors** — both implement structural pruning to reduce LLM size and latency, but LLM-Pruner offers a general pruning framework applicable to multiple architectures, while LLM-Shearing proposes a specific pre-training-aware pruning approach optimized for LLaMA models.

LLM-Pruner
47
Emerging
LLM-Shearing
42
Emerging
Maintenance 0/25
Adoption 10/25
Maturity 16/25
Community 21/25
Maintenance 0/25
Adoption 10/25
Maturity 16/25
Community 16/25
Stars: 1,109
Forks: 130
Downloads: —
Commits (30d): 0
Language: Python
License: Apache-2.0
Stars: 642
Forks: 57
Downloads: —
Commits (30d): 0
Language: Python
License: MIT
Stale 6m No Package No Dependents
Stale 6m No Package No Dependents

About LLM-Pruner

horseee/LLM-Pruner

[NeurIPS 2023] LLM-Pruner: On the Structural Pruning of Large Language Models. Support Llama-3/3.1, Llama-2, LLaMA, BLOOM, Vicuna, Baichuan, TinyLlama, etc.

About LLM-Shearing

princeton-nlp/LLM-Shearing

[ICLR 2024] Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning

Related comparisons

LLM-Pruner and LLaMA-Pruning LLM-Pruner and LLaMA-Pruning

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