Automorphic is a platform that enhances language models with knowledge infusion tools, such as Conduit, and conversion tools like TREX. It provides security measures with Aegis, designed to protect against adversarial attacks. The Automorphic Hub allows users to access models trained and refined on the platform for their usage, supporting seamless integration with the OpenAI API.
Conduit, a tool by Automorphic, infuses knowledge into language models by allowing the fine-tuning of models, dynamically combining specific behavior or knowledge adapters, and rapidly iterating on models through human-in-the-loop feedback, thus streamlining deployment.
Furthermore, Automorphic integrates seamlessly with the OpenAI API, offering compatibility with existing codebases and enabling users to leverage its features without altering their current code. TREX, another tool provided by Automorphic, converts unstructured data into structured formats as defined by users, such as JSON, XML, YAML, or other custom formats specified by regular expressions or context-free grammars.
Automorphic prioritizes user and model protection against adversarial attacks, PII leakage, and toxic language through its firewall tool, Aegis, which continuously learns from usage to enhance detection and defense mechanisms over time.
Overall, Automorphic's features, including Conduit for knowledge infusion, TREX for data transformation, and Aegis for security, contribute to a secure, efficient, and flexible user experience with language models.
Automorphic was created by an organization that prioritizes model and user protection, particularly against adversarial attacks, prompt and PII leakage, and toxic language. The platform was launched on July 31, 2024, and the company founder and detailed company information are not explicitly mentioned in the provided documents. Automorphic offers tools like Conduit for knowledge infusion, TREX for data transformation, and Aegis for security measures. The platform allows easy access to models trained on Automorphic and supports integration with the OpenAI API, ensuring flexibility and adaptability for users.
To use Automorphic effectively, follow these steps:
Understand Automorphic: Familiarize yourself with Automorphic, a platform that enhances language models with tools like Conduit for knowledge infusion and TREX for data conversion, ensuring security with Aegis against adversarial attacks.
Utilize Conduit: Learn how Conduit improves language models by infusing knowledge and enabling rapid model iteration through human feedback, fine-tuned adapters, and dynamic model combination.
Explore Automorphic Hub: Access publicly shared models on the Automorphic Hub, facilitating model training and refinement for users.
Leverage TREX: Utilize TREX to convert unstructured data into structured formats like JSON, XML, and more, offering high customization and predictable output.
Secure with Aegis: Protect language models and users with Aegis, safeguarding against adversarial attacks, PII leakage, and toxic language.
Integration: Integrate Automorphic seamlessly with the OpenAI API without modifying existing codebases for enhanced compatibility.
Continuous Learning: Benefit from Automorphic's continuous learning features, allowing for model updates based on user feedback and manual labeling for improved model performance over time.
By following these steps, you can effectively harness the capabilities of Automorphic for enhancing language models, ensuring data security, and continuous model improvement.
I appreciate the knowledge infusion capabilities of Conduit. It allows for fine-tuning models with specific data, which has enhanced the performance of our applications.
The integration process with the OpenAI API can be a bit cumbersome at times, requiring adjustments in existing code that can be tricky.
Automorphic helps in transforming unstructured data into structured formats efficiently, which saves us a lot of time in data preprocessing.
The security features provided by Aegis are quite impressive. It's reassuring to know that our models are protected against adversarial attacks.
The platform feels quite limited in terms of available documentation and support. It can be difficult to troubleshoot issues without adequate resources.
The ability to convert unstructured data to structured formats is beneficial, but the execution could be smoother. It sometimes requires manual adjustments.
I love the TREX tool for data transformation. It simplifies the process of handling different data formats and saves us a lot of effort.
Sometimes the user interface feels a bit outdated, which can make navigation less intuitive than it could be.
It helps streamline our data conversion processes, making it easier to handle and analyze large datasets, which increases our productivity.
