> ## Documentation Index
> Fetch the complete documentation index at: https://docs.oumi.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# RUNNING TRAINING
> Easily launch fine-tuning jobs
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.
## HOW TO RUN TRAINING JOBS
You can either select`Supervised 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](/training/on-policy-distillation) for more information regarding configuration optionss and settings.
## 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.