How can I slim down my LLM agents?
THE HIDDEN BLOAT IN MACHINE LEARNING SYSTEMS
This paper isn't about multi-agent AI. It focuses on reducing software bloat (unnecessary code) in Machine Learning (ML) frameworks, particularly those used for deep learning and Large Language Models (LLMs). The tool developed, Negativa-ML, analyzes shared libraries used by ML frameworks like PyTorch and TensorFlow to identify and remove code not needed for a specific task. It analyzes both host (CPU) and device (GPU) code, with a particular focus on device code bloat, which has been largely overlooked in prior research. While not directly related to multi-agent systems, the research's focus on optimizing LLM frameworks could indirectly benefit complex LLM-based applications, including potential multi-agent scenarios, by improving efficiency and resource usage. Specifically, the smaller binaries created by removing bloat could help deploy LLMs in resource-constrained environments, which might be relevant for some multi-agent systems where agents are deployed on devices with limited resources.