From Layers to Submodules: Rethinking Granularity in Replacement-Based LLM Compression

1. 1Analyze the architecture of a Large Language Model to identify redundant components
2. 2Apply replacement-based compression methods to remove or replace entire layers
3. 3Experiment with non-contiguous selection and submodule granularity to improve compression efficiency
4. 4Evaluate the performance of the compressed model using metrics such as accuracy and inference time
5. 5Refine the compression approach based on the evaluation results

AI engineers and researchers on a team can benefit from this knowledge to optimize their LLMs, while data scientists can apply these concepts to other machine learning models

💡 Redundancy in pretrained transformers is not limited to contiguous regions, allowing for more flexible compression approaches

💡 Rethink LLM compression granularity to improve efficiency and performance