Optimizing Large Language Models: An Experimental Approach to Pruning and Fine-Tuning LLama2 7B

In the face of high computational demands from large language models (LLMs), we present an experimental approach to model pruning and fine-tuning to overcome these resource challenges. Navigate through our systematic process of turning a 7 billion parameter LLama2 model into a practical 1.5 billion parameter variant. Learn the iterative sequence for layer removal and realignment, backed by extensive experimentation on truncation techniques. We demonstrate how these compact models can serve as efficient drafters, providing rapid responses while their larger versions address more sophisticated tasks.