Code for the paper "SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot" with LLaMA implementation.

[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.

Prune a model while finetuning or training.

A simple and effective LLM pruning approach.

Code for the ICML 2023 paper "SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot".

A curated list for Efficient Large Language Models

A block pruning framework for LLMs.

Official Implementation of SLEB: Streamlining LLMs through Redundancy Verification and Elimination of Transformer Blocks

BlockDrop: Dynamic Inference Paths in Residual Networks

Collection of recent methods on (deep) neural network compression and acceleration.

GNN-RL Compression: Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learning

RL-Pruner: Structured Pruning Using Reinforcement Learning for CNN Compression and Acceleration