About this document
Depth Pruning for Efficient LLMs by ashwinkumarss is a document available to read on EtoBox.
The document presents a study on a depth pruning method for large language models (LLMs) that aims to improve inference efficiency, particularly under memory constraints. It compares this approach with traditional width pruning techniques, demonstrating that depth pruning can achieve competitive performance and faster inference speeds in scenarios with limited batch sizes. The findings suggest that depth pruning is a viable strategy for optimizing LLMs for deployment on local and edge devices.
- Author
- ashwinkumarss
- Language
- EN