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Oumi training recipes provide guided workflows for launching and managing model training runs quickly and consistently. Instead of configuring every parameter from scratch, recipes package best practices into reusable templates that simplify setup and reduce errors. Whether you’re fine-tuning a base model, running instruction tuning, or experimenting with different configurations, recipes help you move from dataset to trained model with minimal friction.

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What is a training Recipe?

A training recipe is a reusable configuration for running model training. Instead of manually reconfiguring each run, you define a structured setup that specifies:
  • Which base model to fine-tune
  • Which datasets to use (training, validation, test)
  • What training method to apply (e.g., FFT or PEFT/LoRA)
  • Which hyperparameters to use (learning rate, steps, batch size, etc.)
You can think of a recipe as a training blueprint. The model and datasets are the inputs, the training method and hyperparameters define the process, and the recipe ensures each run is consistent and repeatable. This approach allows you to:
  • Standardize training configurations across teams and projects
  • Reproduce results reliably
  • Compare experiments with controlled changes
  • Iterate quickly by adjusting parameters without redefining the entire setup
By separating configuration from execution, training recipes make fine-tuning structured, reproducible, and aligned with measurable performance goals.

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Saving & accessing training Recipes

When starting a training run, the Oumi Builder gives you the option to save the configuration as a recipe. You can access it later anytime from the Recipes page. To save a training recipe:
  1. After configuring your inputs, click the save icon.
  2. Give your recipe a Display Name and click Save Recipe.
You can now access and run this recipe from the Recipes page.