COMPOT is a training‑free compression framework for transformer models that uses a small calibration dataset to estimate a sparse weight factorization. It employs orthogonal dictionaries, enabling closed‑form Procrustes updates and analytical single‑step sparse coding, which dramatically reduces optimization cost. The method also introduces a one‑shot dynamic allocation strategy to adaptively redistribute compression rates across layers, achieving a superior quality‑compression trade‑off compared to strong low‑rank and sparse baselines while remaining compatible with post‑training quantization.
Modern transformers pair impressive capabilities with substantial memory and compute demands. Low-rank weight factorization reduces both while keeping the matrices dense, and thus efficient on standar...
The paper introduces 3BASiL-TM, a one‑shot post‑training method for compressing large language models using a sparse plus low‑rank decomposition. It employs a novel 3‑block ADMM algorithm to minimize layer‑wise reconstruction error and a transformer‑matching refinement that jointly optimizes sparse and low‑rank components across layers. Experiments show a >30% reduction in WikiText2 perplexity gap for LLaMA‑8B under a (2:4 sparse + 64 LR) setting and a 2.5× faster compression runtime on an A100 GPU compared to state‑of‑the‑art methods.
arXiv:2609.40127v1 Announce Type: cross Abstract: Modern transformers pair impressive capabilities with substantial memory and compute demands. Low-rank weight factorization reduces both while keepin...
The paper introduces GaugeLasso, a method that applies symmetric group‑lasso penalties to transformer channels during training, enabling entire tensor slices to be zeroed out while maintaining dense tensors for GPU efficiency. By calibrating channel penalties based on inference utility per compute, the network self‑organizes into depth‑dependent structural profiles that can be dramatically smaller than the original architecture, achieving up to 255‑fold compression on a polynomial division task and outperforming hand‑designed baselines on language modeling and autoencoding benchmarks. The approach also accelerates training and reveals over‑provisioned axes that guide subsequent design iterations.
arXiv:2411. 09816v5 Announce Type: replace Abstract: Large neural networks achieve state-of-the-art performance on many tasks, yet their sheer size hinders deployment on resource-constrained devices.
The paper introduces the "Compression Trinity," a unified framework that jointly applies sparsity, quantization, and low‑rank approximations to compress large language models. It presents several methods—MKOR, SLoPe, OPTIMA, PATCH, and SLiM—that leverage these three pillars to accelerate training, reduce memory bandwidth, and recover accuracy, achieving significant speedups and accuracy gains over existing techniques. The results demonstrate that combining all three compression strategies is essential for efficient, scalable, high‑performance LLM deployment.
Prohibitive computational and environmental costs impede the scalable deployment of Large Language Models (LLMs). Traditional compression techniques (sparsity, quantization, low-rank approximations) a...
arXiv:2610.32009v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models reduce computation through sparse activation, but storing their expert parameters remains a major memory bottleneck. We...
Activation-Aware Weight Tensorization (AWT) is a training‑free calibration wrapper that preconditions each weight matrix with a diagonal activation‑derived scale before applying standard Tensor Train (TT) or Tree Tensor Network (TTN) decompositions. By adding only an input‑side elementwise rescaling, AWT improves functional fidelity of post‑training tensor‑network compression across several large language models (Llama 3.1 8B, Ministral 8B, Qwen2.5 7B) at 2–6× compression, closing a significant portion of the perplexity gap to dense baselines and enhancing downstream task performance. The method demonstrates that diagonal preconditioning offers a robustness‑modularity trade‑off, outperforming full‑covariance oracles in many cases while remaining modular and solver‑agnostic.
arXiv:2608.30384v1 Announce Type: new Abstract: By introducing RSLM (Rotated Scaled Lloyd-Max), a family of training-free vector quantization codecs compressing embeddings to 1--4 bits per dimension,...
arXiv:2509. 25136v3 Announce Type: replace Abstract: Activation-aware low-rank factorization techniques yield strong compression results but are generally confined to linear layers, while existing whitening-based theory typically makes an implicit full-rank assumption on activations.
arXiv:2606. 00573v1 Announce Type: new Abstract: Vision-language models (VLMs) deliver strong multimodal reasoning capabilities, but their large computational cost and high parameter counts make deployment challenging on resource-constrained devices.