This project demonstrates a loose implementation of the "Mixture of Agents" concept using Llama APIs. The approach involves creating multiple instances of a Llama model to independently generate responses (proposer models) and then aggregating these responses into a single, coherent answer using another Llama model (aggregator). Each layer progressively refines the response by taking the initial user query and the previous layer's output.
- Layered Architecture: Uses multiple layers to refine responses through deeper reasoning.
- Proposer Models: Three instances of Llama models that independently generate responses.
- Aggregator Model: Aggregates the responses from proposer models into a single, high-quality response.
- Python 3.x
- Llama API