Skip to main content
1

Add the OpenPipe Integration

Navigate to Settings -> Connections in your Helicone dashboard and configure the OpenPipe integration.
Configure OpenPipe Integration
This integration allows you to manage your fine-tuning datasets and jobs seamlessly within Helicone.
2

Create a Dataset for Fine-Tuning

Your dataset doesn’t need to be enormous to be effective. In fact, smaller, high-quality datasets often yield better results.
  • Recommendation: Start with 50-200 examples that are representative of the tasks you want the model to perform.
Create a new dataset
Ensure your dataset includes clear input-output pairs to guide the model during fine-tuning.
3

Evaluate and Refine Your Dataset

Within Helicone, you can evaluate your dataset to identify any issues or areas for improvement.
  • Review Samples: Check for consistency and clarity in your examples.
  • Modify as Needed: Make adjustments to ensure the dataset aligns closely with your desired outcomes.
Evaluate your dataset
Regular evaluation helps in creating a robust fine-tuning dataset that enhances model performance.
4

Configure Your Fine-Tuning Job

Set up your fine-tuning job by specifying parameters such as:
  • Model Selection: Choose the base model you wish to fine-tune.
  • Training Settings: Adjust hyperparameters like learning rate, epochs, and batch size.
  • Validation Metrics: Define how you’ll measure the model’s performance during training.
Configure your fine-tuning job
After configuring, initiate the fine-tuning process. Helicone and OpenPipe handle the heavy lifting, providing you with progress updates.
5

Deploy and Monitor Your Fine-Tuned Model

Once fine-tuning is complete:
  • Deployment: Integrate the fine-tuned model into your application via Helicone’s API endpoints.
  • Monitoring: Use Helicone’s observability tools to track performance, usage, and any anomalies.

​
Additional Fine-Tuning Resources

For more information on fine-tuning, check out these resources: