UniCuCo learns a request-to-pruning-strategy mapping with a Gaussian-process surrogate, serving many arbitrary compression requests in under a second each with accuracy close to per-request evolutionary search.
Transactions of the Associa- tion for Computational Linguistics, 12:1556–1577
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CL 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
One-for-All Pruning: A Universal Model for Customized Compression of Large Language Models
UniCuCo learns a request-to-pruning-strategy mapping with a Gaussian-process surrogate, serving many arbitrary compression requests in under a second each with accuracy close to per-request evolutionary search.