Online Structured Pruning of LLMs via KV Similarity
Analysis
This ArXiv paper likely explores efficient methods for compressing Large Language Models (LLMs) through structured pruning techniques. The focus on Key-Value (KV) similarity suggests a novel approach to identify and remove redundant parameters during online operation.
Key Takeaways
- •Focus on structured pruning for LLM compression.
- •Utilizes Key-Value (KV) similarity as a core technique.
- •Implies online pruning, enabling dynamic model optimization.
Reference / Citation
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