SlimLLM: Accurate Structured Pruning for Large Language Models

A new method called SlimLLM is proposed to efficiently prune large language models by evaluating the importance of entire channels and attention heads, enabling better compression with minimal performance loss. The approach, validated on the LLaMA benchmark, outperforms existing methods and achieves state-of-the-art results in structured pruning of LLMs.