The landscape of open-source large language models (LLMs) has become increasingly crowded, making it challenging for developers to navigate the options and find the best model for their specific use case. To help with this, we'll explore some of the top open-source LLMs, evaluating them based on factors such as performance, size, ease of use, and suitability for various tasks like writing, coding, and fine-tuning. The models have been compared in terms of quality, speed, cost, and other metrics to provide a comprehensive overview of their capabilities. We also discuss the differences between instruct and non-instruct versions, 7B and 70B parameter counts, and how to serve these models blazingly fast using platforms like Modal.