llm-pruning-collection
Enrichment pendingA collection of various llm pruning implementations, training code for GPUs & TPUs, and evaluation script.
GraphCanon updated today · GitHub synced today
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Steady (85d since push)
- As of today
- Provenance
- Not a fork · Organization account
- As of today
- Security (OSV)
- No lockfile
- As of today
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install llm-pruning-collection PyPISimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A collection of various llm pruning implementations, training code for GPUs & TPUs, and evaluation script.
Capability facts
- Languages
- python
Source: github.language · Jul 15, 2026
Categories
Tags
README
Small LLMs: Pruning vs Training from Scratch
Yufeng Xu1, Taiming Lu1, Jiachen Zhu2, Mingjie Sun3, Kunjun Li1, and Zhuang Liu1
1 Princeton. 2 NYU. 3 CMU.
This is a Jax-based repo for LLM Prunning, It contains:
- the implementations of various LLM pruning methods of different granularity.
- pretraining and fine-tuning code for both GPU and TPU platforms.
- evaluation scripts for assessing model performance.
We gratefully acknowledge the generous support of the Google TPU Research Cloud (TRC), which provided the computational resources used to build this repository.
The repo is organized as follows:
├── pruning
│ ├── FLAP # including Wanda-sp and FLAP
│ ├── LLM-Pruner
│ ├── llmshearing # sheared llama
│ ├── minitron # including shortgpt
│ ├── SLEB # including sleb
│ ├── SliceGPT # including slicegpt
│ └── wanda # including sparsegpt and magnitude pruning
├── training
│ ├── fms_fsdp
│ └── maxtext
└── eval
where pruning is the collection of the pruning methods we experimented; training contains the LLM training frameworks we used, and we provided options for both TPU and GPU; eval contains JAX-compatible eval scripts we used to evaluate the pruned models.
Supported Features
Pruning Methods
- Minitron
- ShortGPT
- Wanda
- SliceGPT
- SparseGPT
- Magnitude
- Sheared Llama
- SLEB
- LLM Pruner
- FLAP
Training Frameworks
- FMS-FSDP
- MaxText
Evaluation
- accelerate lm-eval-harness for maxtext. (by 2-4x times!)
Get Started
Pruning
In order to reproduce the results of the different pruning methods, we need to set up separate environments for different methods. The installation and command guide can be found at pruning/<method>/README.md. Below is an overview:
Minitron
cd pruning/minitron
bash scripts/install.sh
bash scripts/prune_llama3.1-8b.sh # contains minitron depth and width for llama3.1-8b
ShortGPT
cd pruning/minitron
bash scripts/install.sh
bash scripts/prune_llama2-7b.sh
Wanda, SparseGPT, Magnitude
cd pruning/wanda
bash scripts/install.sh
bash scripts/prune_llama3.1-8b.sh # contains wanda, sparsegpt, and magnitude for llama3.1-8b
bash scripts/prune_llama2-7b.sh
bash scripts/prune_llama-7b.sh
LLM-Pruner
cd pruning/LLM-Pruner
bash scripts/install.sh
bash scripts/prune_llama-7b.sh
bash scripts/prune_llama2-7b.sh
bash scripts/prune_llama3.1-8b.sh
Sheared Llama
cd pruning/llmshearing
bash scripts/install.sh
mkdir -p llmshearing/data/red_pajama && cd llmshearing/data/red_pajama
huggingface-cli download Zephyr271828/redpajama-for-prune --repo-type dataset --local-dir for_prune
cd -
bash scripts/hf2composer.sh
bash scripts/prune_llama2-2.7b.sh
bash scripts/prune_llama2-1.3b.sh
bash scripts/prune_llama2-370m.sh
bash scripts/composer2hf.sh
Training
GPU To train on GPUs, please refer to the guide of fms-fsdp for details.
TPU To train on TPUs, please refer to guide of MaxText for details.
Evaluation
GPU
For evaluation on GPUS, you may run the following evaluation script on your HF checkpoint:
cd training/fms_fsdp
bash scripts/install.sh
cd ../../eval
bash scripts/eva
For agents
This page has a .md twin and JSON over the API.