opensearch-project
[META] Support model fine tune
Model fine-tuning is the process of further training a pre-trained machine learning model on a specific dataset or task. This technique allows the model to adapt its knowledge to a particular domain or application, improving its performance on that specific task.
It improves accuracy on specific tasks while requiring less time and resources than training from scratch. This process enables transfer learning, allowing models to apply pre-existing knowledge to new domains. Fine-tuning facilitates customization for specific use cases and can achieve good results with smaller datasets. It helps overcome domain shift and can address biases present in pre-trained models. The technique supports continuous learning, keeping models up-to-date with new data. Fine-tuning is cost-effective and versatile, enabling a single pre-trained model to be adapted for various tasks. Ultimately, it allows organizations to create more accurate and specialized AI models tailored to their specific needs and data.
