> For the complete documentation index, see [llms.txt](https://docs.entrypointai.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.entrypointai.com/guides/fine-tune-a-model/start-a-fine-tune.md).

# Start a Fine-tune

Once you have a dataset with enough examples (see [Quantity & Quality of Data](/guides/build-a-dataset/quantity-and-quality-of-data.md)) and have connected a [model provider](/key-concepts/model-providers.md) that supports fine-tuning, you are ready to fine-tune a model.

To start a fine-tune, go to Models, press the + button, and choose "Start a fine-tune."

<figure><img src="https://3106255375-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F03h3SC78wyaaTZz7oUet%2Fuploads%2FLFwKjN3tJOPQxy12Rwqo%2Fannotely_image%20(10).jpeg?alt=media&amp;token=b1739c57-5fd1-4a7e-9f82-0e645e86f24b" alt=""><figcaption></figcaption></figure>

On the next screen, you can select your [template](/key-concepts/templates.md) and [model provider](/key-concepts/model-providers.md). You can also see the token counts and estimated time and/or cost if available.

{% hint style="info" %}
For most fine-tuning jobs, the template should be very minimal. You do not need to include a lengthy instructional prompt, although in small datasets a very short prompt or semantic labels for your field references can help cue desired behavior.
{% endhint %}

In the next section, we'll discuss common fine-tuning hyperparameters and how to set them.