> ## Documentation Index > Fetch the complete documentation index at: https://docs.helicone.ai/llms.txt > Use this file to discover all available pages before exploring further. # How to fine-tune LLMs with Helicone and OpenPipe > Learn how to fine-tune large language models with Helicone and OpenPipe to optimize performance for specific tasks. 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. 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. 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. Configure your fine-tuning job

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. ## Additional Fine-Tuning Resources For more information on fine-tuning, check out these resources: * [Fine-Tuning Best Practices: Training Data](https://openpipe.ai/blog/fine-tuning-best-practices-series-introduction-and-chapter-1-training-data) * [Fine-Tuning Best Practices: Models](https://openpipe.ai/blog/fine-tuning-best-practices-chapter-2-models) * [How to use OpenAI fine-tuning API](/faq/openai-fine-tuning-api) * [Understanding fine-tuning duration](/faq/llm-fine-tuning-time) * [Comparing RAG and fine-tuning approaches](/faq/rag-vs-fine-tuning)