6 papers · 1 filter
Two For The Price Of One: Curvature-Aware Pruning Preserves Influence Functions Too
Mehdi Makni, Natalia Ponomareva, Rahul Mazumder
Post-training pruning enables efficient deployment, but conventional methods do not explicitly preserve internal-gradient alignment between sparse and dense models. We study novel…
ThinQuant: Scalable Rotation Learning for Weight and Activation Quantization of LLMs
Mehdi Makni, Ryan Lucas, Rahul Mazumder
Learned rotations play an important role in enabling low-bit weight and activation quantization of large language models by smoothing outliers in the activation distribution. State…
ADMM-Q: An Improved Hessian-based Weight Quantizer for Post-Training Quantization of Large Language Models
Ryan Lucas, Mehdi Makni, Xiang Meng +2
Quantization is an effective strategy to reduce the storage and computation footprint of large language models (LLMs). Post-training quantization (PTQ) is a leading approach for co…
3BASiL: An Algorithmic Framework for Sparse plus Low-Rank Compression of LLMs
Mehdi Makni, Xiang Meng, Rahul Mazumder
Sparse plus Low-Rank decomposition of Large Language Models (LLMs) has emerged as a promising direction in model compression, aiming to decompose pre-t…
TSENOR: Highly-Efficient Algorithm for Finding Transposable N:M Sparse Masks
Xiang Meng, Mehdi Makni, Rahul Mazumder
Network pruning reduces the computational requirements of large neural networks, with N:M sparsity -- retaining only N out of every M consecutive weights -- offering a compelling b…
An Optimization Framework for Differentially Private Sparse Fine-Tuning
Mehdi Makni, Kayhan Behdin, Gabriel Afriat +5
Differentially private stochastic gradient descent (DP-SGD) is broadly considered to be the gold standard for training and fine-tuning neural networks under differential privacy (D…