OpenAI Fine-Tune

OpenAI Enhances Generative AI Model Fine-Tuning and Custom Model Services

OpenAI is upgrading the Custom Model program for enterprise customers that tailors its generative AI models to serve their specific needs. The enhancement will also open up opportunities to developers interested in deepening their control over fine-tuning the models and improving their performance and effectiveness.

Fine-Tune AI

Fine-tuning allows for a more precise adaptation of the models to specific content areas, enhancing their performance on particular tasks while optimizing cost and reducing latency. OpenAI’s new iteration adds automated checkpoints for training models, a comparative playground for model evaluation, third-party integration for sharing fine-tuning data, quality assessment metrics, and a better management dashboard. The upgraded platform is built on the success of the self-serve fine-tuning API for GPT-3.5 OpenAI released last August. The company claims to have since signed up thousands of organizations to train hundreds of thousands of models for everything from writing code to adapting content to user behavior, like Indeed’s fine-tuned model for matching job seekers to open positions.

OpenAI is also expanding the Custom Model program unveiled at its DevDay conference. As the name suggests, Custom Model clients are looking to integrate an AI model into specific domains and work with OpenAI to gain the benefit of more advanced fine-tuning techniques. That includes components of the Assisted Fine-Tuning offering, which sets up a collaborative effort with OpenAI’s technical teams to employ more complex fine-tuning techniques like hyperparameters and parameter-efficient fine-tuning (PEFT) methods at a larger scale. This is particularly helpful for organizations that need support with efficient training data pipelines, evaluation systems, and bespoke parameters and methods to maximize model performance for their use case or task.OpenAI pitches the Custom Model program at organizations with unique needs and lot of data who want to train fully custom models from scratch. For instance, legal tech startup Harvey has a generative AI tool for lawyers has a model immersed in case law.

“We believe that in the future, the vast majority of organizations will develop customized models that are personalized to their industry, business, or use case. With a variety of techniques available to build a custom model, organizations of all sizes can develop personalized models to realize more meaningful, specific impact from their AI implementations,” OpenAI explained in a blog post. “With OpenAI, most organizations can see meaningful results quickly with the self-serve fine-tuning API. For any organizations that need to more deeply fine-tune their models or imbue new, domain-specific knowledge into the model, our Custom Model programs can help.”

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