In this guide we will demonstrate how to use Humanloop’s fine-tuning workflow to produce improved models leveraging your user feedback data.


  1. You already have a project created - if not, please pause and first follow our project creation guides.
  2. You have integrated humanloop.generate() and with the API or Python SDK.

A common question is how much data do I need to fine-tune effectively? Here we can reference the OpenAI guidelines:

The more training examples you have, the better. We recommend having at least a couple hundred examples. In general, we've found that each doubling of the dataset size leads to a linear increase in model quality.

The first part of finetuning is to select the data you wish to finetune on.

  1. Go to your Humanloop project and navigate to Data tab.
  2. Create a filter (using the + Filter button above the table) of the datapoints you would like to fine-tune on.
    1. For example, all the datapoints that have received a positive upvote in the feedback captured from your end users.

  1. Now click the New fine-tuned model button to set up the finetuning process.
  2. Enter the appropriate parameters for the finetuned model.
    1. Enter a Model name. This will be used as the suffix parameter in OpenAI’s finetune interface. For example, a suffix of "custom-model-name" would produce a model name like ada:ft-your-org:custom-model-name-2022-02-15-04-21-04.
    2. Choose the Base model to finetune. This can be ada, babbage, curie, or davinci.
    3. Select a Validation split percentage. This is the proportion of data that will be used for validation. Metrics will be periodically calculated against the validation data during training.
    4. Enter a Data snapshot name. Humanloop associates a data snapshot to every fine-tuned model instance so it is easy to keep track of what data is used (you can see yourexisting data snapshots on the Settings/Data snapshots page)

  1. Click Create. The fine-tuning process runs asynchronously and may take up to a couple of hours to complete depending on your data snapshot size.
  2. Navigate to the Fine-tuning tab to see the progress of the fine-tuning process.
    Coming soon - notifications for when your fine-tuning jobs have completed.

  1. When the Status of the finetuned model is marked as Successful, the model is ready to use.

🎉 You can now include this finetuned model in a new model config for your project to evaluate its performance. You can use the Playground or SDK in order to achieve this.