What does it mean in AI - Inference After Fine-tuning

In AI, inference after fine-tuning refers to the process of using a model to make predictions or decisions (inference) after it has been fine-tuned for a specific task.

Here’s a breakdown of the terms:

Fine-tuning: This is a process where a pre-trained model (often trained on a large, general dataset) is further trained (fine-tuned) on a smaller, task-specific dataset. The goal of fine-tuning is to adapt the pre-trained model to perform well on a specialized task, like identifying certain types of objects in images or analyzing sentiment in text.
Inference: Once a model is trained (or fine-tuned), inference refers to using the model to make predictions on new, unseen data. It’s the phase where the model is applied to real-world data to generate outputs, such as classifying an image or predicting the next word in a sentence.

So, inference after fine-tuning means running the model on new data to make predictions or decisions after it has been specifically trained (fine-tuned) for a given task. The fine-tuning helps the model become more accurate and specialized for that particular use case.

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