This paper proposes a preference-aligned routing framework for LLMs that guides model selection by matching queries to user-defined domains or action types. It introduces Arch-Router, a compact 1.5B model that learns to map queries to domain-action preferences for model routing decisions, outperforming proprietary models in subjective evaluation criteria.
- Shows how an individual can use Python for speech recognition (SR), Push-to-- Talk (PTT) systems, and large action models to create their own AI assistant.
- Describes the process of creating a "la Rabbit prototype" with Python code on Raspberry Pi hardware.
- Emphasizes how these components can be combined for various tasks using different APIs like OpenAI or LLaMA (Large Language Model from Meta).
- Describes the process of creating a "la Rabbit prototype" with Python code on Raspberry Pi hardware.
- Emphasizes how these components can be combined for various tasks using different APIs like OpenAI or LLaMA (Large Language Model from Meta).
