Quantization of a Llama Language Model for improved Efficiency and Inference

Despite their transformational potential, large language models (LLMs) like Llama are difficult to implement on devices with limited computational power due to their high computational requirements. This study explores the quantization of the Lamba model, a method that minimizes memory footprint and model size for effective deployment. In order to obtain significant model compression with acceptable performance, we investigate different quantization techniques. At various quantization levels, the study will assess the trade-off between efficiency and accuracy. We will also look into how quantization affects the target devices' power consumption and inference speed. By enabling deployment on resource-constrained platforms and effectively quantifying the Llama model, this initiative seeks to democratize access to potent AI tools, encouraging greater innovation and practical applications. Additionally, a smaller model results in cheaper implementation costs and enhanced sustainability due to lower inference power usage. In order to quantified the Llama model, this research explores a number of technical approaches, assesses performance trade-offs, and optimizes deployment for effective hardware use. This project's objective is to successfully quantify the Llama model in order to show that it is feasible to implement it in contexts with limited resources. The results will help create LLMs that are easier to use and more effective.