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Welcome to the Constellation of Models (CoM) repository, where we introduce a groundbreaking approach to tackle complex, multi-modal tasks using artificial intelligence. The CoM system is designed to dynamically distribute tasks to a constellation of specialized, pre-trained models, each expert in its respective domain.
- The Constellation of Models (CoM) Approach
- Router Nomenclature
- Models and Routers on Different Hardware Systems
- Security: Firewall at the Prompt Level
- Managing Multiple Models
- AI Model Types and Security Vulnerabilities
- Contact Information
The CoM approach is a flexible and specialized solution for multi-model strategies. By setting a standard based on this approach, we can ensure consistency and efficiency in situations where multiple models must be utilized. The dynamic router serves as a skillful orchestrator, routing each query to the most capable model, optimizing performance and efficiency.
The router nomenclature presented in this repository offers a clear and coherent naming convention for a wide range of AI models and tasks. The nomenclature adheres to a simple pattern, with router names prefixed by "CoM_" followed by a succinct descriptor of the model's function or task.
The CoM approach accommodates the distribution of models across multiple servers, harnessing unique computational power and specialized capabilities. This distributed architecture allows for scalability, efficiency, and enhanced performance in AI systems.
To protect AI models from potential threats, a firewall at the prompt level is implemented, acting as a barrier that filters and monitors incoming requests to the AI models, ensuring that only legitimate and safe queries are processed.
As AI models proliferate, the CoM approach provides an intelligent management system that dynamically orchestrates tasks and data among models, ensuring optimal performance and resource allocation. Additionally, a framework is established to monitor, secure, and validate the behavior of each model within the CoM ecosystem.
The repository provides an overview of various AI model types and their associated security vulnerabilities, such as computer vision, natural language processing, audio processing, reinforcement learning, and tabular data processing models.
For any inquiries or additional information, please contact nielzac[@]proton.me.
FineTune Code used : https://github.com/geronimi73/phi2-finetune/blob/main/nb_qlora.ipynb HTML RIKTIK