Efficient Compression of Large Language Models: A Case Study on Llama 2 with 13B Parameters

Abstract Efficient compression of large language models is important for enhancing computational efficiency and reducing the necessary virtual memory requirements for deployment in resource-limited environments. Employing a combination of advanced compression techniques—pruning, quantization, and knowledge distillation—this investigation explores the viability of reducing model size while maintaining high performance levels. Pruning reduces the number of non-critical parameters, significantly lowering the computational load. Quantization decreases the precision of the numerical data within the model, thus reducing the memory footprint without substantially affecting the model's ability to process data effectively. Knowledge distillation involves training a smaller, more compact model to replicate the predictive performance of the larger model, ensuring minimal loss of capability. The results demonstrate that the application of these techniques not only considerably enhances the speed of data processing but also reduces the operational costs and energy consumption, thereby supporting the deployment of powerful AI models in a wider variety of settings. The exploration confirms that while there is a slight reduction in accuracy and F1-score, the trade-offs are justifiable given the benefits gained in processing efficiency and resource utilization. By meticulously optimizing the balance between compression intensity and model performance, tailored solutions can be developed to meet specific operational needs, thereby expanding the practical applications of large language models in real-world scenarios. The outcomes underscore the transformative potential of model compression technologies to advance the field of artificial intelligence by enabling more sustainable and efficient AI deployments across diverse computational environments.