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.

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.

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

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.