Benchmarking Large Language Models for Code Generation

Abstract

As the landscape of software development continues to evolve, the need for efficient and innovative coding practices becomes increasingly apparent. This research endeavors to explore the effectiveness of Large Language Models (LLMs) in code generation, focusing on benchmarking their performance across various coding tasks. Leveraging advanced Natural Language Processing (NLP) techniques and deep learning architectures, our study investigates how LLMs, such as the codellama-13b-instruct.Q5_K_S.gguf engine, interpret and generate code from natural language instructions. With an emphasis on accuracy, efficiency, and user accessibility, our research seeks to shed light on the capabilities of LLMs in bridging the gap between human language and executable code. By evaluating factors such as model architecture, training data quality, and task complexity, we aim to provide insights into the potential of LLMs for revolutionizing the coding experience. Through meticulous benchmarking and analysis, this study aims to contribute to the advancement of LLM development and its applications in code generation, paving the way for more efficient and inclusive coding practices in the future.