GroupMask: Layer-Adaptive Group-wise Sparsity for Semi-Structured LLM Pruning
cs.LG, cs.AI
Submitted: 2026-09-27
Updated: 2026-09-27
Code: https://github.com/ZhengaoLi/GroupMask
Terminology
Sources
- SliceGPT: Compress Large Language Models by Deleting Rows and Columns
- Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation
- PIQA: Reasoning about Physical Commonsense in Natural Language
- Differentiable Architecture Search with Ensemble Gumbel-Softmax
- Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
- GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
- Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
- What Matters in Transformers? Not All Attention is Needed
- Distilling the Knowledge in a Neural Network
- PATCH: Learnable Tile-level Hybrid Sparsity for LLMs
- Categorical Reparameterization with Gumbel-Softmax
- Training Recipe for N:M Structured Sparsity with Decaying Pruning Mask
- ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs
- Accelerating Sparse Deep Neural Networks
- PyTorch: An Imperative Style, High-Performance Deep Learning Library
- WinoGrande: An Adversarial Winograd Schema Challenge at Scale
- A Simple and Effective Pruning Approach for Large Language Models
- Llama 2: Open Foundation and Fine-Tuned Chat Models
- BESA: Pruning Large Language Models with Blockwise Parameter-Efficient Sparsity Allocation
- Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity
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