TRIM: Achieving Extreme Sparsity with Targeted Row-wise Iterative Metric-driven Pruning

• URL: http://arxiv.org/abs/2505.16743v1
• Date: Thu, 22 May 2025 14:53:53 GMT
• Title: TRIM: Achieving Extreme Sparsity with Targeted Row-wise Iterative Metric-driven Pruning
• Authors: Florentin Beck, William Rudman, Carsten Eickhoff,
• Abstract summary: Large Language Models (LLMs) present significant computational and memory challenges due to their extensive size.<n>Existing one-shot pruning methods often apply uniform sparsity constraints across layers or within each layer.<n>This work introduces a novel approach that applies varying sparsity ratios to individual output dimensions (rows) within each layer.
• Score: 16.47847349673178
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) present significant computational and memory challenges due to their extensive size, making pruning essential for their efficient deployment. Existing one-shot pruning methods often apply uniform sparsity constraints across layers or within each layer, resulting in suboptimal performance, especially at high sparsity ratios. This work introduces TRIM (Targeted Row-wise Iterative Metric-driven pruning), a novel approach that applies varying sparsity ratios to individual output dimensions (rows) within each layer. TRIM employs an iterative adjustment process guided by quality metrics to optimize dimension-wise sparsity allocation, focusing on reducing variance in quality retention across outputs to preserve critical information. TRIM can be seamlessly integrated with existing layer-wise pruning strategies. Our evaluations on perplexity and zero-shot tasks across diverse LLM families (Qwen2.5, LLaMA-2, and OPT) and sparsity levels demonstrate that TRIM achieves new state-of-the-art results and enhances stability. For instance, at 80% sparsity, TRIM reduces perplexity by 48% for Qwen2.5-14B and over 90% for OPT-13B compared to baseline methods. We conclude that fine-grained, dimension-wise sparsity adaptation is crucial for pushing the limits of extreme LLM compression. Code available at: https://github.com/flobk/TRIM

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