# 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.

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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).


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