Overview

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.

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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).