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  • Getting Started
    • Overview
    • Why Humanloop?
    • Quickstart Tutorial
  • Tutorials
    • Create your first GPT-4 App
    • ChatGPT clone with streaming
  • Guides
    • Create a Prompt
    • Fine-tune a model
    • Manage API keys
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    • Deploy to environments
  • Core concepts
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    • Datasets
    • Evaluators
    • Logs
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  • Reference
    • Supported Models
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  • Prerequisites
  • Fine-tuning
Guides

Fine-tune a model

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

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Prerequisites

  • You already have a Prompt — if not, please follow our Prompt creation guide first.
  • You have integrated humanloop.complete_deployed() or the humanloop.chat_deployed() endpoints, along with the humanloop.feedback() 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.

Fine-tuning

The first part of fine-tuning is to select the data you wish to fine-tune on.

1

Go to your Humanloop project and navigate to Logs tab.

2

Create a filter

Using the + Filter button above the table of the logs you would like to fine-tune on.

For example, all the logs that have received a positive upvote in the feedback captured from your end users.

3

Click the Actions button, then click the New fine-tuned model button to set up the finetuning process.

4

Enter the appropriate parameters for the fine-tuned model.

  1. Enter a Model name. This will be used as the suffix parameter in OpenAI’s fine-tune 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 fine-tune. 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)
5

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.

6

See the progress

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.

7

When the Status of the fine-tuned model is marked as Successful, the model is ready to use.

🎉 You can now use this fine-tuned model in a Prompt and evaluate its performance.