Agent Tuning Optimization Framework Demo

A framework for efficiently tuning LLMs into specialized agents using negative and synthetic samples

About This Framework

The Agent Tuning Optimization Framework provides a comprehensive solution for efficiently tuning large language models into specialized agents through the strategic incorporation of negative samples and synthetic trajectories.

Key Features:

1. Negative Sample Generation: Creates examples of undesired agent behaviors to teach models what not to do
2. Synthetic Trajectory Generation: Automatically generates diverse interaction trajectories
3. Mixed-Sample Tuning: Combines positive examples, negative samples, and synthetic trajectories
4. Parameter-Efficient Fine-Tuning: Implements methods like LoRA for computational efficiency

This demo provides a simplified simulation of the framework's capabilities. For full functionality, deploy the complete framework following the provided documentation.