⚙️Model Fine-tuning

What is Model Fine-tuning?

Fine-tuning is the process of training a base language model on a dataset to perform better in a specific domain or for a target use case. By leveraging the foundational knowledge already embedded in the model, fine-tuning allows the model to specialize in tasks like customer support automation, medical text classification, or legal document summarization. This approach significantly reduces the time and resources needed compared to training a model scratch, while still delivering high accuracy and relevance.

To meet this growing demand, Model Fine-tuning is built by FPT Smart Cloud to be user-friendly, enabling AI customization through a simple interface on the FPT AI Factory Portal. Users can upload their dataset, configure training hyperparameters, and set up infrastructure - all within a few clicks

Thanks to this streamlined approach, Model Fine-tuning empowers organizations to unlock the full potential of AI, delivering smarter, faster, and more accurate solutions tailored to their unique business needs.

When to Use Model Fine-tuning?

Model Fine-tuning is useful when:

• You want the model to understand domain-specific knowledge (e.g., medical, legal, financial).

• You want better performance on a specific task (e.g., translation, summarization, code generation).

• You need the model to match a specific tone and style (e.g., formal writing, brand voice).

• You need higher accuracy than prompt engineering or beddings can provide.

But Model Fine-tuning is not needed when:

• Your task can be solved with prompt engineering or one-shot/ few-shot examples.

• You only need to filter/classify content (embedding models + classifiers may suffice).