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Oumi streamlines model fine tuning and performance iteration by providing multiple training methods and flexible configuration options. This allows you to experiment efficiently while retaining full control over your setup.

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How to run training jobs

You can either selectSupervised Fine-Tuning to train a model using labeled examples, or On-policy Distillation to train a student model using a teacher model for knowledge distillation.

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Supervised fine-tuning (SFT)

To start an SFT job, initiate a training run from the Models page.
  1. Click on Train New Model.
  2. In the Builder, select Supervised Fine-Tuning.
  3. Select the base model to fine-tune. Oumi offers a broad range of commonly used models.
  4. Choose your training dataset, and optionally select validation and test datasets. You can use uploaded datasets, synthesized data, or merged datasets.
  5. Select a training method. Oumi supports full fine-tuning (FFT) and parameter-efficient fine-tuning (PEFT), including LoRA.
  6. Adjust advanced hyperparameters (e.g., maximum steps, learning rate) if needed.
  7. Review your configuration, (optionally) save it as a reusable recipe, and launch the training job.

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On-policy distillation

To start an on-policy distillation job, initiate a training run from the Models page.
  1. Click on Train New Model.
  2. In the Builder, select On-Policy Distillation.
  3. Leave Training Method on On-Policy Distillation.
  4. Choose your Base Model and Teacher Model.
  5. Select your Training Dataset.
  6. Configure advanced settings (e.g., Training Settings, Distillation Settings, Parameter-Efficient Settings) if needed.
Please see On-Policy Distillation for more information regarding configuration optionss and settings.

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Checking job status

After a training job launches, it will appear on the Activity log page with a status of Running. When training completes, you can access your model from the Model page.