Adaptive-LLM is a TieSet’s framework that leverages smaller local models to improve the efficiency and performance of Large Language Models (LLMs) that perform even better for various tasks by not sharing any sensitive data to anybody.
The essential knowledge from the bigger LLM is extracted and transferred to smaller and more resource-friendly local models. This LLM functionality extraction process into smaller LLMs enhances task-specific performance while reducing computational requirements.
The framework also incorporates a context module that dynamically assigns appropriate contexts to the local models, allowing it to adapt to diverse tasks effectively. Additionally, the framework utilizes data vectors for efficient storage and retrieval of task-specific data, optimizing processing speed and memory consumption.