4 papers · 1 filter
Freeze Deep, Train Shallow: Interpretable Layer Allocation for Continued Pre-Training
Yu-Hang Wu, Qin-Yuan Liu, Qiu-Yang Zhao +3
Selective layer-wise updates are essential for low-cost continued pre-training of Large Language Models (LLMs), yet determining which layers to freeze or train remains an empirical…
FreqKV: Key-Value Compression in Frequency Domain for Context Window Extension
Jushi Kai, Yixuan Wang, Boyi Zeng +4
Existing key-value (KV) cache compression methods for large language models (LLMs) often rely on token eviction, which risks losing critical local information in both long prefilli…
TreeKV: Smooth Key-Value Cache Compression with Tree Structures
Ziwei He, Jian Yuan, Haoli Bai +2
Efficient key-value (KV) cache compression is critical for scaling transformer-based Large Language Models (LLMs) in long sequences and resource-limited settings. Existing methods…
WeightedKV: Attention Scores Weighted Key-Value Cache Merging for Large Language Models
Jian Yuan, Ziwei He, Haoli Bai +2
Large Language Models (LLMs) use key-value (KV) cache to reduce redundant computation in autoregressive generation. However, the KV cache size increases linearly during generation,…