DaMoC combines data filtering, token compression, and layer pruning to select the best LLM for domain fine-tuning, claiming ~20x faster training while preserving model rankings.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CL 1years
2025 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression
DaMoC combines data filtering, token compression, and layer pruning to select the best LLM for domain fine-tuning, claiming ~20x faster training while preserving model rankings.