Promote Sign in Sign up

Database Q&A

Automorphic ✓

Infuse knowledge into language models with minimal samples.

652 views 4 saves Unknown
Language Model Natural Language Understanding NLP Improvements
More categories (4)
Linguistics Text Processing Semantic Analysis Database Q&A
Visit website Sign in to save

Verdict

A quick read before you dig into the full page.

Best if
Infuse knowledge into language models with minimal samples
Skip if
Complex setup for TREX
Pick this when
Fine-tunes language models

Considering Defog instead? Compare side by side

Overview

Automorphic is a tool called Conduit that enables the infusion of knowledge into language models. It overcomes the limitations of prompt stuffing by allowing fine-tuning of language models, training adapters for behavior or knowledge, and combining them dynamically.

This tool facilitates rapid iteration on models by incorporating human-in-the-loop feedback and streamlining model deployment.Conduit improves language models by continuously updating them based on user feedback and manual labeling.

It ensures high performance and deployment efficiency by enabling quick loading and stacking of fine-tuned adapters. Compatibility with the OpenAI API allows users to seamlessly integrate Conduit into their existing codebase.The Automorphic Hub serves as a platform where publicly shared models trained and enhanced using Automorphic are made available for inference.

The hub provides access to these models, allowing users to leverage them for their own applications.Another tool offered by Automorphic is TREX, which converts unstructured data into a structured format of the user's choice, such as JSON, XML, YAML, or any other format defined by a regular expression or context-free grammar.

TREX offers a highly customizable alternative to OpenAI's functions.Aegis is a firewall tool provided by Automorphic to protect language models and users from adversarial attacks.

It defends against prompt injections, prompt and personal identifiable information (PII) leakage, and toxic language. Aegis continuously learns from usage to enhance its detection capabilities over time.

Automorphic website

Access & connections

Where you can use Automorphic.

Platforms

Web

Capabilities

What this tool accepts, produces, and is strongest at.

Input

  • Text
  • API

Output

  • Text
  • API

Pros & cons

Pros

  • Fine-tunes language models
  • Combines models dynamically
  • Allows rapid model iteration
  • Incorporates human feedback
  • Streamlined model deployment
  • Continuous model updates
  • Enables quick adapter loading
  • Stacks fine-tuned adapters
Show 18 more
  • Publicly shared models hub
  • Converts unstructured data
  • Supports JSON, XML, YAML
  • Supports other formats via regex/context-free grammar
  • Detects adversarial attacks
  • Prevents PII leakage
  • Filters toxic language
  • Learns from usage
  • Protects against prompt injections
  • Adapts to new threats over time
  • Compatible with existing codebase
  • 100% predictable output
  • User-customized structuring of unstructured data
  • Provides data protection
  • Automates data structuring
  • Shares enhanced models publicly
  • Generates valid json objects
  • Supports customized grammars

Cons

  • Requires manual feedback
  • Adapts slow to initial feedback
  • Complex setup for TREX
  • No multi-language support
  • Limited information on Aegis mechanism
  • No clarity on update frequency

Compare alternatives

Similar tools in the catalog — with a clear reason to open each one.

Free

Full comparison

Free + from $5/mo

Full comparison

from $1000/mo

Full comparison

Free + from $99/mo

Full comparison

Why look Higher community rating

Full comparison

Why look Higher community rating

Full comparison

Releases 1

Initial release June 28, 2023 Current
Initial release of Automorphic.

FAQ 12

What is Automorphic?
Automorphic is a platform that enhances language models with knowledge infusion tools, such as Conduit, and conversion tools like TREX. It also provides security measures with Aegis, a tool designed to protect against adversarial attacks. The Automorphic Hub allows users to access models trained and refined on the platform for their own usage. Furthermore, Automorphic's tools support seamless integration with the OpenAI API, ensuring flexibility and adaptability for users.
What is Conduit and how does it improve language models?
Conduit is a tool by Automorphic specifically designed to infuse knowledge into language models. It bypasses the inefficiencies and limitations of traditional prompt stuffing, allowing the fine-tuning of language models. Conduit also enables the training of adapters for specific behavior or knowledge, which can be dynamically combined. Crucially, it provides the ability to rapidly iterate on models through incorporating human-in-the-loop feedback, which helps streamline model deployment. Furthermore, Conduit augments user datasets to improve models continuously, ensuring they remain self-improving.
How does Conduit enable quick loading and stacking of fine-tuned adapters?
Conduit enables the quick loading and stacking of fine-tuned adapters by facilitating efficient data handling and processing. By eliminating concerns over performance and deployment, it allows users to focus on fine-tuning models and providing feedback. This results in reduced waiting times and increased operational efficiency.
Can Conduit be integrated easily into my already existing codebase?
Absolutely, Conduit can be integrated easily into already existing codebases. It is declared to be compatible with the OpenAI API, meaning users can use it with no changes to their current codes.
What is the purpose of the Automorphic Hub?
The Automorphic Hub serves as the platform where publicly shared models that have been trained and normalized on Automorphic are made available for inference. It acts as a repository for these models, facilitating easy access and utilization for users.
Can I access models from other users on the Automorphic Hub?
Yes, the Automorphic Hub allows users to access models that have been shared publicly. These models have been trained and improved using Automorphic and can be used for inference in their applications.
What is TREX and what advantages does it offer over OpenAI's functions?
TREX is a tool by Automorphic that transforms unstructured data into a structured format as per the user's choice. With TREX, the output from language models becomes 100% predictable as it is converted into user-selected structured formats. Unlike OpenAI's default functions, TREX offers high customization options, giving users more control and flexibility over their data processing.
Can TREX convert unstructured data into JSON or XML?
Yes, TREX can indeed convert unstructured data into either JSON or XML, offering a comprehensive range of structured formats to choose from, which includes but is not limited to JSON and XML. These might even be formats defined by a regular expression or context-free grammar.
What is Aegis and how does it protect language models and users?
Aegis is essentially a firewall tool provided by Automorphic, designed to protect language models and users from adversarial attacks. Aegis effectively defends against prompt injections, prompt and personal identifiable information (PII) leakage, and also toxic language. This ensures the integrity of the models and ensures user data security.
Does Aegis have the capability to detect toxic language and PII leakage?
Yes, it is a core function of Aegis to detect toxic language and PII leakage. Aegis is designed to safeguard against these particular threats alongside additional measures for defending against prompt injections.
How does Aegis improve its detection capabilities over time?
Aegis improves its detection capabilities over time by learning from usage. This means the more it is used, the more effective it becomes at identifying and countering adversarial attacks, enhancing its protective measures over time.
What is meant by 'infusing knowledge into language models'?
Infusing knowledge into language models is the process of fine-tuning these models to include specific knowledge or behavior. This is achieved through training adapters which are then dynamically combined. It overcomes the inefficiencies and limitations of traditional prompt stuffing methods.
Automorphic
Visit website