The paper, "Model Swarms," proposes a novel algorithm for adapting large language models (LLMs) by employing swarm intelligence. This method involves creating a "swarm" of LLM experts that collaboratively search for optimal model weights. Unlike existing model composition techniques that rely on tuning data or specific assumptions, Model Swarms enables flexible adaptation to various objectives, including single-task performance, multi-task domains, reward models, and human interests, with minimal data requirements. The research highlights the emergence of previously unseen capabilities in the swarm, demonstrating the potential for "weak-to-strong" transitions in LLM experts through the collaborative search process.