Automorphic provides a platform that enables the fine-tuning of language models using minimal data samples, allowing for efficient knowledge infusion and rapid model iteration. Its Conduit technology facilitates real-time updates based on user feedback, ensuring models continuously improve while maintaining compatibility with the OpenAI API and supporting on-premise deployment for data security.
Funding
Funding not disclosed
Founders
Product
Problem
Fine-tuning language models for specific tasks or knowledge domains typically requires large datasets and significant computational resources. Traditional methods of knowledge infusion, such as prompt engineering, are often inefficient and limited by context window constraints. Updating models with real-time user feedback can be challenging, hindering continuous improvement and adaptation.
Solution
Automorphic offers a platform that enables efficient fine-tuning of language models using minimal data samples, facilitating rapid knowledge infusion and model iteration. Their Conduit technology allows for real-time model updates based on user feedback, ensuring continuous improvement and adaptation to evolving requirements. The platform supports the creation and management of adapters for specific behaviors or knowledge domains, which can be combined and applied dynamically. Automorphic's solution bypasses context-window limitations by fine-tuning, and offers compatibility with the OpenAI API, allowing seamless integration with existing workflows. For enhanced data security, on-premise deployment options are available.
Target Audience
The primary target audience includes organizations and developers seeking to efficiently customize and improve language models for specific applications, while maintaining data security and minimizing computational costs.
Features
- Fine-tuning with as few as 10 data samples
- Conduit technology for real-time model updates based on user feedback
- Adapter-based architecture for modular knowledge and behavior infusion
- Dynamic adapter combination and commutation for flexible model customization
- Compatibility with the OpenAI API for seamless integration
- On-premise deployment option for enhanced data security
- Rapid loading and stacking of fine-tuned adapters for performance optimization
- Dataset augmentation for continuous model improvement