As of July 2024, open-source large language models (LLMs) such as Mistral Large 2, Llama 3.1, and Command R+ are leading benchmarks, showcasing their versatility for fine-tuning across diverse applications. The open-source LLM ecosystem has rapidly evolved, with models like Gemma 2, Nemotron-4, and Llama 3.1 surpassing proprietary counterparts in versatility and performance. The LLM landscape is enriched by models developed through community efforts and new foundation model groups, emphasizing the importance of selecting the right model for production systems. Several models have been discontinued due to adoption challenges and resource reallocation, while new entries like Mixtral, Tuli, and Yi have expanded the ecosystem. The guide provides insights into running and fine-tuning open-source LLMs locally and in production environments, highlighting tools, platforms, and performance metrics. This transformation in the open-source LLM landscape underscores the democratization of AI advancements, offering developers and organizations a broader array of options to leverage state-of-the-art language models.