Large Language Models (LLMs) have been making headlines in the AI world for their ability to perform a wide range of natural language processing tasks. They are designed to estimate the probability of a sequence of words, given the preceding words. This ability has made them useful in a variety of applications, including chatbots, language translation, and text summarization. In recent years, there has been remarkable growth in the capability of open-source LLMs due to several factors, including the increasing availability of data, the development of new training techniques, and the growing demand for AI solutions. Their transparent, accessible, and customizable ability makes them a perfect alternative for closed-source LLMs such as GPT-4 . One of the challenges with open-source LLMs is no agreed-upon evaluation criteria. It makes it difficult to compare and choose a model for a particular task. However, several benchmarking techniques have come forth, such as MMLU and ARC, to evaluat