Fine-tuning open and closed models with custom hyperparameters only takes a few clicks.
Before you begin: Before training your first model, make sure you’ve created a dataset and imported at least 10 training entries.

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Training a Model

1

Navigate to Dataset

To train a model, navigate to the dataset you’d like to train your model on. Click the Fine Tune button in the top right corner of the General tab.
2

Name your Model

Choose a descriptive name for your new model. This name will be used as the model parameter when querying it in code. You can always rename your model later.
3

Select Base Model

Select the base model you’d like to fine-tune on. We recommend starting with Llama 3.1 8B if you aren’t sure which to choose.
4

Adjust Hyperparameters (optional)

Under Advanced Options, you can optionally adjust the hyperparameters to fine-tune your model. You can leave these at their default values if you aren’t sure which to choose.
5

Start Training

Click Start Training to begin the training process. The training job may take a few minutes or a few hours to complete, depending on the amount of training data, the base model, and the hyperparameters you choose.
To learn more about fine-tuning through the webapp, check out the Fine-Tuning via Webapp page. To learn about fine-tuning via API, see our Fine Tuning via API page.