Fine-Tuning

To perform fine-tuning of an Archetype AI model to produce a custom model for your use case, use the Fine-Tuning dashboard in the Developer Console.

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Creating a Fine-Tuning Job

Upon choosing the Fine-Tuning dashboard option in the sidebar, the Create Fine-Tuning Job modal appears. Enter a human-readable name for the fine-tuning job, then select the base model that you wish to fine-tune into a custom model. Next, choose the method by which the base model should be adapted to your data. The methods available depend on the base model you’ve selected. Some models may only offer one method. The adaptation methods are described in the user interface; click the method’s name to see a description. After selecting the base model, the modal expands to include options to configure the fine-tuning job further, such as specifying training files, any validation files that are needed, and hyperparameter settings.

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Specifying Training and Validation Files

Specifying training files for the fine-tuning job is done similarly to specifying files anywhere in the Archetype Console: you can provide files in one of three ways:

• By selecting files already present in your library of files
• By entering the file IDs (file names) of existing files in your library
• By uploading new files to your library; doing so automatically adds them to the selected files list as well as adding them to your file library

If using the Omega model, the list of selected files also lets you edit the class labels used for each corresponding file. Validation files are added next, using a similar interface. These files are required by the Omega model and optional for the Newton C-model.

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Specifying Hyperparameters

The exact hyperparameters available may vary depending on the base model and/or adaptation method you selected. To learn more about a given hyperparameter, highlight the icon next to its name to see a tooltip describing the hyperparameter. All hyperparameters assume an automatic default value, but you can adjust fine-tuning by setting particular values that suit your use case and input data. To specify a custom value for a hyperparameter, first check the Custom checkbox next to the hyperparameter’s value input box, then enter the custom value for the hyperparameter. In the above example, a custom value of 32 is specified for the Step Size hyperparameter.

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Advanced Settings

Finally, review the available advanced settings to see if there are any you need to set up. These are all optional. The advanced settings are: For more information about these advanced settings, or for information about advanced settings not listed above, highlight the icon next to the setting’s name to see a descriptive tooltip. Once all settings are configured, click the Create Job button to create the job and enqueue it to be run.

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Monitoring a Fine-Tuning Job

To see a list of existing fine-tuning jobs and their current statuses, click Fine-Tuning in the Developer Console dashboard’s sidebar. To copy the name or job ID of a job, click the clipboard icon next to its name or ID when you mouse over a row in the job list. To view details about a job, click anywhere else in the row. This opens the details view for the job. At the top is the job’s name and current status. Next to this is the Pause or Resume button. The remainder of the details view is divided into several boxes. The Overview box lists basic information about the job, including:

• The job ID
• The base model on which the fine-tuned model will be based
• The ID of the fine-tuned model (available only once the fine-tuning job has successfully completed)
• The checkpoint ID from which the job was warm-started
• The starting timestamp
• The ending timestamp (available only once the job has completed)
• The total amount of time the job took to run (available only once the job has completed)

The Parameters box lists basic configuration information about the job, including:

• The number of training files specified
• The number of validation files specified
• The suffix that will be applied to the new fine-tuned model’s name
• The random number seed that was specified, if any

The Training box shows the progress of the training process. This information is updated periodically as the training progresses, and is displayed using a tabbed interface.

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Pausing and Resuming a Fine-Tuning Job

To pause a fine-tuning job, open its details view. At the top of the view, to the right of the fine-tuning job’s name and status, is the Pause button. Click Pause to pause the job. The job will pause, freeing resources to be used by other jobs, and the button’s name changes to Resume. Click the Resume button to continue the job from where it left off.

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Using a Fine-Tuned Model

Once fine-tuning has completed, open the Checkpoints tab in the fine-tuning job’s screen, then click the clipboard icon next to the checkpoint ID you wish to use as the model for your batch job. This will copy the checkpoint ID to your clipboard. Then, go to the Batch Manager and set up a new job. Before starting the job, set the Fine-tuned checkpoint field in the Create Batch Job panel to the ID of your model’s checkpoint (by pasting the copied ID, for example). This field is located immediately after where the source files (such as input files and N-shot example files) are specified. When you start the job, it will do so using your fine-tuned model.