- Automorphic is creating waves in language model fine-tuning by using 10 samples to infuse knowledge.
- Using Automorphic, you can say goodbye to long and tiring prompt surfing as it bypasses context-window limitations, enhancing model capabilities at an optimal cost.
- Through rapid iteration and human-in-the-loop feedback for continuous model improvement, Automorphic is revolutionizing Language Model Fine- Tuning.
Today's modern world is brimming with data-driven applications that rely upon language models as their core.
Sure enough, it has always been challenging how best to update these models with new knowledge. Prompt stuffing has its weaknesses and disadvantages.
This is why Govind Gnanakumar, Mahesh Natamai, and Maaher Gandhi founded Automorphic in 2023 to rethink language model fine-tuning.
How does Automorphic work?
Automorphic relies on the self-improvement modus operandi. It can flawlessly include new information into models by making adapters for behavior or knowledge instead of focusing on context windows.
This process is super-fast because fine-tuned adapters can be loaded rapidly and stacked.
Whether you provide feedback manually or through labeled inference requests, Automorphic updates your models swiftly, ensuring they stay relevant and accurate.
What Sets Automorphic Apart?
- The Most Obscure Models for Maximum Privacy: Automorphic offers the most obscure models, prioritizing user privacy and data security.
- Data structuring custom-made firewalls: Change unstructured information into customizable structured formats that improve model performance and flexibility.
- Versatile deployment options: Automorphic provides flexible deployment options, either on-premise or through the OpenAI API which are customized to suit your needs in terms of data sovereignty and compliance.
Driving Efficiency and Effectiveness in Language Model Fine-Tuning
Any business can now forget about inefficient and costly language model fine-tuning with Automorphic.
With just 10 samples, context-window bypassing, and smooth integration within current processes, Automorphic helps organizations unlock the full potential of language models.
Whether it is about privacy improvements, improving the accuracy of models, or simplifying their deployment process, Automorphic has always been the most effective way to maximize language models’ value in today’s world dominated by data.
Edited By Annette George.
