Fine Tuning

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Using the UI

When navigating to the Models page, you can filter the listed models by the ones that are eligible for fine tuning.

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Fine-Tunable Models

Supported Models for Fine Tuning

As of today (July 2024) we support only OpenAI models for fine tuning in Scale GP

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Training Datasets

In order to be able to fine tune a model on the platform, you will need a training dataset. To create a new training dataset, navigate to “Training Datasets”. A Training Dataset consists of a set of input and output pairs.

Scale GP typically comes with a few pre-installed training datasets for typical tasks like RAG, Coding or Creative Writing. If you have a local installation of Scale GP without pre-installed training datasets, please contact Scale to get these datasets installed in your account. While each use case is different, a fine-tuning dataset should include at least include a couple of hundred prompt response pairs in order to have a meaningful impact on model performance for the respective domain.

If you have a dataset of prompt response pairs ready to go, you can hit “New Dataset” and upload the dataset directly via the UI.

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New Fine Tune

After deciding for a training dataset, you can navigate back to “Models” and click “New Fine Tune” in the top right to being a fine tuning job. Subsequently, you can simply walk through the steps in the wizard to create the fine tune by picking the model type, base model to fine-tune, providing a name (and optionally description) and finally selecting a training dataset.

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Fine Tuning Jobs

After completing the setup, you’ll be able to monitor the status of the fine tuning job by navigating to “Fine-Tuning Jobs.

After the fine tuning job is completed, the fine-tuned model will appear in “Models” with a prefix of “ft:” and is ready to be used by the completion API or as part of an SGP application.

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Using the API

You can also create new fine-tuning jobs directly using the SGP API, for details please refer to the API Reference.