Prune, Update and Trim: Robust Structured Pruning for Large Language Models
Large Language Models (LLMs) have experienced significant growth and development in recent years. However, performing inference on LLMs remains costly, especially for long-context inference or in resource-constrained devices. This motivates the development of new post-training pruning (PTP) methods. These methods reduce LLMs' requirements by removing a substantial part of the model's parameters. The discarded weights are selected depending on their impact on the models performance. Current PTP methods prune the models by removing the less informative hidden nodes from the FFN layers, and the least important attention layers. We propose Putri, a PTP method that introduces three changes to ...
| Authors | Diego Coello de Portugal Mecke, Tom Hanika, Lars Schmidt-Thieme |
|---|---|
| Primary category | cs.LG |
| Published | 18 May 2026 |
| Last updated at source | 13 July 2026 |
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