Automorphic: Empowering Language Models with Enhanced Safety and Efficiency

Automorphic offers a suite of tools designed to improve the performance, safety, and efficiency of language models. Its core offering, Conduit, revolutionizes model training and deployment by enabling fine-tuning, adapter training, and dynamic combination. This allows for rapid iteration and seamless integration with existing workflows, particularly those utilizing the OpenAI API.

Features

• Conduit: A powerful tool for infusing knowledge into language models, overcoming the limitations of prompt stuffing. It supports fine-tuning, adapter training, and dynamic combination, enabling rapid model iteration via human-in-the-loop feedback.
• Automorphic Hub: A platform hosting publicly shared models trained and enhanced using Automorphic, providing ready-to-use models for various applications.
• TREX: A versatile data conversion tool that transforms unstructured data into structured formats like JSON, XML, and YAML, offering a customizable alternative to OpenAI's functions.
• Aegis: A robust firewall protecting language models and users from adversarial attacks, including prompt injections, PII leakage, and toxic language. Aegis continuously learns and adapts to enhance its protection.

Benefits

• Improved Model Performance: Fine-tuning and adapter training lead to higher accuracy and better performance.
• Increased Efficiency: Streamlined model deployment and quick loading of adapters enhance efficiency.
• Enhanced Safety: Aegis provides robust protection against adversarial attacks, ensuring secure model usage.
• Rapid Iteration: Human-in-the-loop feedback and easy integration accelerate the development process.

Use Cases

• Custom Language Model Development: Build and deploy highly specialized language models for specific tasks.
• Data Transformation: Convert unstructured data into usable structured formats.
• Safe and Secure Model Deployment: Protect models and users from malicious attacks.

Automorphic provides a comprehensive solution for building, deploying, and securing high-performance language models.