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PreprintarXiv licencearXiv

IrekoGPT: Turning Structured Pruning into Post-Hoc Slimmable LLMs

Abstract

We introduce IrekoGPT, a post-hoc method for converting pretrained LLMs into slimmable models whose width can be adjusted at inference time. Building on SliceGPT, we retain its projection matrices without pruning them, allowing a single model to expose nested subnetworks at different widths. We improve robustness by calibrating each layer across multiple compression ratios, and correct downstream linear layers through gradient-free ridge regression. Across Llama and Qwen models, preliminary results show improvements over naive PCA-based slimming, with the largest gains at high compression. Code is available at https://github.com/aimagelab/IrekoGPT

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BibTeX
@misc{moriello2026irekogpt,
  title = {{IrekoGPT: Turning Structured Pruning into Post-Hoc Slimmable LLMs}},
  author = {Pietro Moriello and Pietro Buzzega and Angelo Porrello and Simone Calderara},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.00426},
  eprint = {2610.00426},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.00426},
}
RIS
TY  - UNPB
TI  - IrekoGPT: Turning Structured Pruning into Post-Hoc Slimmable LLMs
AU  - Pietro Moriello
AU  - Pietro Buzzega
AU  - Angelo Porrello
AU  - Simone Calderara
PY  - 2026
DA  - 2026/10/01
PB  - arXiv
DO  - 10.48550/arxiv.2610.00426
UR  - https://arxiv.org/abs/2610.00426
AB  - We introduce IrekoGPT, a post-hoc method for converting pretrained LLMs into slimmable models whose width can be adjusted at inference time. Building on SliceGPT, we retain its projection matrices without pruning them, allowing a single model to expose nested subnetworks at different widths. We improve robustness by calibrating each layer across multiple compression ratios, and correct downstream linear layers through gradient-free ridge regression. Across Llama and Qwen models, preliminary results show improvements over naive PCA-based slimming, with the largest gains at high compression. Code is available at https://github.com/aimagelab/IrekoGPT
ER  -