Revolutionizing Language Model Fine-Tuning With Automorphic
- Automorphic uses 10 samples to infuse knowledge into language models.
- Its approach bypasses context-window limitations and reduces the need for long prompt surfing.
- Automorphic uses rapid iteration and human-in-the-loop feedback for continuous model improvement.
Modern data-driven applications rely on language models as a core part of their operations. Updating these models with new knowledge, however, can be challenging. Prompt stuffing has weaknesses and disadvantages.
Govind Gnanakumar, Mahesh Natamai, and Maaher Gandhi founded Automorphic in 2023 to rethink language model fine-tuning.
How Does Automorphic Work?
Automorphic uses a self-improvement approach to add new information to language models. Instead of focusing on context windows, it creates adapters for behavior or knowledge.
These fine-tuned adapters can be loaded rapidly and stacked, supporting a fast fine-tuning process.
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Users can provide feedback manually or through labeled inference requests. Automorphic then updates the models based on that feedback.
What Sets Automorphic Apart?
- Privacy-focused models: Automorphic offers models described in the original article as “the most obscure models,” with a focus on user privacy and data security.
- Custom data structuring: Automorphic changes unstructured information into customizable structured formats intended to improve model performance and flexibility.
- Flexible deployment: Automorphic supports deployment on-premise or through the OpenAI API, with options customized for data sovereignty and compliance requirements.
Language Model Fine-Tuning With 10 Samples
Automorphic’s language model fine-tuning approach uses 10 samples and bypasses context-window limitations.
The platform can also integrate within current processes. Its stated applications include improving model privacy, improving accuracy, and simplifying deployment.
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