Choose Your Model Size: Any Compression of Large Language Models Without Re-Computation

• URL: http://arxiv.org/abs/2502.01717v2
• Date: Sat, 08 Nov 2025 17:15:42 GMT
• Title: Choose Your Model Size: Any Compression of Large Language Models Without Re-Computation
• Authors: Martin Genzel, Patrick Putzky, Pengfei Zhao, Sebastian Schulze, Mattes Mollenhauer, Robert Seidel, Stefan Dietzel, Thomas Wollmann,
• Abstract summary: This work presents Any Compression via Iterative Pruning (ACIP), a novel algorithmic approach to determine a compression-performance trade-off.<n>We use an SVD-reparametrization of linear layers and iteratively prune their singular values with a sparsity-inducing penalty.<n>We show that ACIP seamlessly complements common quantization-based compression techniques.
• Score: 10.376875638696504
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The adoption of Foundation Models in resource-constrained environments remains challenging due to their large size and inference costs. A promising way to overcome these limitations is post-training compression, which aims to balance reduced model size against performance degradation. This work presents Any Compression via Iterative Pruning (ACIP), a novel algorithmic approach to determine a compression-performance trade-off from a single stochastic gradient descent run. To achieve parameter efficiency, we use an SVD-reparametrization of linear layers and iteratively prune their singular values with a sparsity-inducing penalty. Importantly, the pruning order of the parameters is used to derive a global score map that allows compressing a model to any target size without re-computation. We evaluate ACIP on a large selection of open-weight LLMs and downstream tasks, demonstrating state-of-the-art results compared to existing factorization-based compression methods. We also show that ACIP seamlessly complements common quantization-based compression techniques.

Related papers

• Arbitrary Ratio Feature Compression via Next Token Prediction [52.10426317889982]
  Arbitrary Ratio Feature Compression (ARFC) framework supports any compression ratio with a single model.<n>ARC is an auto-regressive model that performs compression via next-gressive prediction.<n>MoS module refines the compressed tokens by utilizing multiple compression results.<n>ERGC is integrated into the training process to preserve semantic and structural relationships during compression.
  arXiv  Detail & Related papers  (2026-02-12T02:38:57Z)
• Gradually Compacting Large Language Models for Reasoning Like a Boiling Frog [72.4168434368873]
  Large Language Models (LLMs) have demonstrated impressive reasoning capabilities, but their substantial size often demands significant computational resources.<n>We propose a gradual compacting method that divides the compression process into multiple fine-grained iterations.<n>This iterative approach-reminiscent of the "boiling frog" effect-enables the model to be progressively compressed without abrupt performance loss.
  arXiv  Detail & Related papers  (2026-02-04T06:56:52Z)
• Rethinking Autoregressive Models for Lossless Image Compression via Hierarchical Parallelism and Progressive Adaptation [75.58269386927076]
  Autoregressive (AR) models are often dismissed as impractical due to prohibitive computational cost.<n>This work re-thinks this paradigm, introducing a framework built on hierarchical parallelism and progressive adaptation.<n> Experiments on diverse datasets (natural, satellite, medical) validate that our method achieves new state-of-the-art compression.
  arXiv  Detail & Related papers  (2025-11-14T06:27:58Z)
• On Information Geometry and Iterative Optimization in Model Compression: Operator Factorization [5.952537659103525]
  We argue that many successful model compression approaches can be understood as implicitly approximating information divergences for this projection.<n>We prove convergence of iterative singular value thresholding for training neural networks subject to a soft rank constraint.
  arXiv  Detail & Related papers  (2025-07-12T23:39:14Z)
• TuneComp: Joint Fine-tuning and Compression for Large Foundation Models [50.33925662486034]
  sequential fine-tuning and compression sacrifices performance, while creating a larger than necessary model as an intermediate step.<n>We propose to jointly fine-tune and compress the model by gradually distilling it to a pruned low-rank structure.<n> Experiments demonstrate that joint fine-tuning and compression significantly outperforms other sequential compression methods.
  arXiv  Detail & Related papers  (2025-05-27T23:49:35Z)
• You Only Prune Once: Designing Calibration-Free Model Compression With Policy Learning [20.62274005080048]
  PruneNet is a novel model compression method that reformulates model pruning as a policy learning process.<n>It can compress the LLaMA-2-7B model in just 15 minutes, achieving over 80% retention of its zero-shot performance.<n>On complex multitask language understanding tasks, PruneNet demonstrates its robustness by preserving up to 80% performance of the original model.
  arXiv  Detail & Related papers  (2025-01-25T18:26:39Z)
• GRASP: Replace Redundant Layers with Adaptive Singular Parameters for Efficient Model Compression [26.51079570548107]
  We propose GRASP (Gradient-based Retention of Adaptive Singular Parameters), a novel compression framework.<n>By replacing redundant layers with only a minimal set of parameters, GRASP achieves efficient compression while maintaining strong performance with minimal overhead.
  arXiv  Detail & Related papers  (2024-12-31T08:22:21Z)
• Singular Value Scaling: Efficient Generative Model Compression via Pruned Weights Refinement [9.454314879815337]
  generative models often exhibit dominant singular vectors, hindering fine-tuning efficiency and leading to suboptimal performance.<n>We introduce Singular Value Scaling (SVS), a versatile technique for refining pruned weights, applicable to both model types.<n>SVS improves compression performance across model types without additional training costs.
  arXiv  Detail & Related papers  (2024-12-23T08:40:08Z)
• Diffusion Product Quantization [18.32568431229839]
  We explore the quantization of diffusion models in extreme compression regimes to reduce model size while maintaining performance. We apply our compression method to the DiT model on ImageNet and consistently outperform other quantization approaches.
  arXiv  Detail & Related papers  (2024-11-19T07:47:37Z)
• LoRC: Low-Rank Compression for LLMs KV Cache with a Progressive Compression Strategy [59.1298692559785]
  Key-Value ( KV) cache is crucial component in serving transformer-based autoregressive large language models (LLMs) Existing approaches to mitigate this issue include: (1) efficient attention variants integrated in upcycling stages; (2) KV cache compression at test time; and (3) KV cache compression at test time. We propose a low-rank approximation of KV weight matrices, allowing plug-in integration with existing transformer-based LLMs without model retraining. Our method is designed to function without model tuning in upcycling stages or task-specific profiling in test stages.
  arXiv  Detail & Related papers  (2024-10-04T03:10:53Z)
• Language Models as Zero-shot Lossless Gradient Compressors: Towards General Neural Parameter Prior Models [56.00251589760559]
  Large language models (LLMs) can act as gradient priors in a zero-shot setting.<n>We introduce LM-GC, a novel method that integrates LLMs with arithmetic coding.<n>Experiments indicate that LM-GC surpasses existing state-of-the-art lossless compression methods.
  arXiv  Detail & Related papers  (2024-09-26T13:38:33Z)
• Activations and Gradients Compression for Model-Parallel Training [85.99744701008802]
  We study how simultaneous compression of activations and gradients in model-parallel distributed training setup affects convergence. We find that gradients require milder compression rates than activations. Experiments also show that models trained with TopK perform well only when compression is also applied during inference.
  arXiv  Detail & Related papers  (2024-01-15T15:54:54Z)
• Rethinking Compression: Reduced Order Modelling of Latent Features in Large Language Models [9.91972450276408]
  This paper introduces an innovative approach for the parametric and practical compression of Large Language Models (LLMs) based on reduced order modelling. Our method represents a significant advancement in model compression by leveraging matrix decomposition, demonstrating superior efficacy compared to the prevailing state-of-the-art structured pruning method.
  arXiv  Detail & Related papers  (2023-12-12T07:56:57Z)
• Lightweight Attribute Localizing Models for Pedestrian Attribute Recognition [13.480231032159834]
  We propose a novel approach for determining the optimal ranks of low-rank layers, ensuring that the gradient direction of the compressed model closely aligns with that of the original model.<n>This means that the compressed model effectively preserves the update direction of the full model, enabling more efficient compression for Pedestrian Attribute Recognition tasks.
  arXiv  Detail & Related papers  (2023-06-16T13:07:13Z)
• Just CHOP: Embarrassingly Simple LLM Compression [27.64461490974072]
  Large language models (LLMs) enable unparalleled few- and zero-shot reasoning capabilities but at a high computational footprint. We show that simple layer pruning coupled with an extended language model pretraining produces state-of-the-art results against structured and even semi-structured compression of models at a 7B scale. We also show how distillation, which has been super effective in task-agnostic compression of smaller BERT-style models, becomes inefficient against our simple pruning technique.
  arXiv  Detail & Related papers  (2023-05-24T08:18:35Z)
• Learning Accurate Performance Predictors for Ultrafast Automated Model Compression [86.22294249097203]
  We propose an ultrafast automated model compression framework called SeerNet for flexible network deployment. Our method achieves competitive accuracy-complexity trade-offs with significant reduction of the search cost.
  arXiv  Detail & Related papers  (2023-04-13T10:52:49Z)
• Compression of Generative Pre-trained Language Models via Quantization [62.80110048377957]
  We find that previous quantization methods fail on generative tasks due to the textithomogeneous word embeddings We propose a token-level contrastive distillation to learn distinguishable word embeddings, and a module-wise dynamic scaling to make quantizers adaptive to different modules.
  arXiv  Detail & Related papers  (2022-03-21T02:11:35Z)
• What do Compressed Large Language Models Forget? Robustness Challenges in Model Compression [68.82486784654817]
  We study two popular model compression techniques including knowledge distillation and pruning. We show that compressed models are significantly less robust than their PLM counterparts on adversarial test sets. We develop a regularization strategy for model compression based on sample uncertainty.
  arXiv  Detail & Related papers  (2021-10-16T00:20:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.