Introducing enhancements to the fine-tuning API and increasing our {custom} fashions program


Assisted Effective-Tuning

At DevDay final November, we introduced a Customized Mannequin program designed to coach and optimize fashions for a particular area, in partnership with a devoted group of OpenAI researchers. Since then, we have met with dozens of consumers to evaluate their {custom} mannequin wants and advanced our program to additional maximize efficiency.

Right this moment, we’re formally asserting our assisted fine-tuning providing as a part of the Customized Mannequin program. Assisted fine-tuning is a collaborative effort with our technical groups to leverage strategies past the fine-tuning API, resembling extra hyperparameters and varied parameter environment friendly fine-tuning (PEFT) strategies at a bigger scale. It’s significantly useful for organizations that want help organising environment friendly coaching knowledge pipelines, analysis programs, and bespoke parameters and strategies to maximise mannequin efficiency for his or her use case or process.

For instance, SK Telecom, a telecommunications operator serving over 30 million subscribers in South Korea, needed to customise a mannequin to be an knowledgeable within the telecommunications area with an preliminary give attention to customer support. They labored with OpenAI to fine-tune GPT-4 to enhance its efficiency in telecom-related conversations within the Korean language. Over the course of a number of weeks, SKT and OpenAI drove significant efficiency enchancment in telecom customer support duties—a 35% improve in dialog summarization high quality, a 33% improve in intent recognition accuracy, and a rise in satisfaction scores from 3.6 to 4.5 (out of 5) when evaluating the fine-tuned mannequin to GPT-4. 

Customized-Skilled Mannequin

In some circumstances, organizations want to coach a purpose-built mannequin from scratch that understands their enterprise, trade, or area. Absolutely custom-trained fashions imbue new data from a particular area by modifying key steps of the mannequin coaching course of utilizing novel mid-training and post-training strategies. Organizations that see success with a completely custom-trained mannequin usually have massive portions of proprietary knowledge—thousands and thousands of examples or billions of tokens—that they need to use to show the mannequin new data or complicated, distinctive behaviors for extremely particular use circumstances. 

For instance, Harvey, an AI-native authorized device for attorneys, partnered with OpenAI to create a custom-trained massive language mannequin for case legislation. Whereas basis fashions had been sturdy at reasoning, they lacked the in depth data of authorized case historical past and different data required for authorized work. After testing out immediate engineering, RAG, and fine-tuning, Harvey labored with our group so as to add the depth of context wanted to the mannequin—the equal of 10 billion tokens value of information. Our group modified each step of the mannequin coaching course of, from domain-specific mid-training to customizing post-training processes and incorporating knowledgeable lawyer suggestions. The ensuing mannequin achieved an 83% improve in factual responses and attorneys most popular the custom-made mannequin’s outputs 97% of the time over GPT-4.


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