# JuryLLM

## Overview
JuryLLM is an experimental framework that orchestrates multiple language models to work collaboratively, similar to a jury system, to solve complex problems. By leveraging the power of ensemble decision-making, this project aims to demonstrate how smaller, open-source LLM models can work together to produce more robust and intelligent solutions.

The major breakthrough in human intelligence occurred when we learned to communicate more effectively. Unlike other highly intelligent species that went extinct, our ability to communicate and collaborate set us apart. The foundations of our progress have always been rooted in effective communication, teamwork, and collective focus toward shared goals. Even the open-source movement embodies this spirit of collaboration, showcasing how working together can drive innovation and success.

## Key Features
- **Model Ensemble**: Integrates multiple language models to work as a collaborative unit
- **Jury-like Decision Making**: Implements a structured approach for models to deliberate and reach consensus
- **Open Source Focus**: Primarily works with accessible, open-source language models
- **Collaborative Intelligence**: Harnesses diverse model perspectives for enhanced problem-solving

## Purpose
The primary goals of JuryLLM are:
- Explore the potential of collaborative AI decision-making
- Demonstrate how smaller models can achieve superior results through teamwork
- Provide an experimental platform for testing ensemble-based approaches
- Create more reliable and well-rounded AI solutions

## Technical Architecture
The system is designed as a modular framework where:
- Multiple language models act as jury members
- Each model contributes its unique perspective
- A coordinated decision-making process synthesizes various inputs
- The final output represents a collective intelligence solution

## Use Cases
- Complex problem-solving requiring multiple perspectives and usinng multiple `specialist models`
- Scenarios where consensus-based decision making is valuable
- Tasks benefiting from diverse model capabilities
- Experimental research in collaborative AI systems

## Contributing
We welcome contributions to this experimental project! Whether you're interested in:
- Adding new model integrations
- Improving the consensus mechanism
- Optimising prompts
- Adding better fine-tuned models
- Enhancing the documentation
- Sharing interesting use cases

## License
MIT License

## Documentation
ToDo

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*Note: This is an experimental project aimed at exploring collaborative AI approaches. The system is under active development and subject to changes.*