OpenAI enhances its fine-tuning API and expands the custom models program, introducing new features for greater developer control and efficiency in AI model training. This initiative supports various applications, from improving job recommendation algorithms to specialized domain training, aiming to enhance performance, reduce costs, and lower latency.
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Introducing improvements to the fine-tuning API and expanding our custom models program
openai.com/blog/introducing-i...
Fine-tuning OpenAI Documentation
Learn how to customize a model for your application.
platform.openai.com/docs/guid...
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Key Takeaways:
✩ Epoch-based Checkpoint Creation: This feature allows the creation of a model checkpoint at each training epoch, reducing the need for retraining and helping to prevent overfitting.
✩ Assisted Fine-Tuning in Custom Models Program: A collaborative effort with OpenAI's technical team to utilize advanced fine-tuning techniques and hyperparameters for optimizing model performance for specific tasks or domains.
✩ Custom-Trained Model Development: For highly specialized needs, OpenAI offers the development of custom-trained models from scratch, using proprietary data to impart unique domain knowledge and capabilities.
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3 апр 2024
