How to fine-tune LLMs with Helicone and OpenPipe

Navigate to Settings -> Connections in your Helicone dashboard and configure the OpenPipe integration.

This integration allows you to manage your fine-tuning datasets and jobs seamlessly within Helicone.

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.

Ensure your dataset includes clear input-output pairs to guide the model during fine-tuning.

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.

Regular evaluation helps in creating a robust fine-tuning dataset that enhances model performance.

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.

After configuring, initiate the fine-tuning process. Helicone and OpenPipe handle the heavy lifting, providing you with progress updates.

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.