# Creating Training Datasets for LLMs

A training dataset is required for you to fine-tune an LLM. To create a training dataset, follow these steps:

1. Log in to the Aizen Jupyter console. See [using-the-aizen-jupyter-console](../getting-started/using-the-aizen-jupyter-console "mention").
2.  Create an ML project if you have not already done so or set the current working project.

    ```
    create project <project name>
    ```

    or

    ```
    set project <project name>
    ```
3.  Configure the dataset by running the `configure dataset` command:

    ```
    configure dataset
    ```
4. In the notebook, you will be guided through a template form with boxes and drop-down lists that you can complete to create features for the dataset.
   * If the input to the LLM is a single column in the dataset, then that column can contain the entire input text, including the prompt, or you can configure a prompt template separately during fine-tuning.
   * If the input to the LLM is two or more columns from the dataset, then you must configure a prompt template separately during fine-tuning.
5.  Create the training dataset using the `start dataset` command to schedule a job. Optionally, you can configure resources for the job by running the `configure resource` command. If you do not configure resources, default resource settings will be applied.

    ```
    configure resource
    start dataset <dataset name>
    ```
6.  Wait for the job to complete, and then check your training dataset:&#x20;

    ```
    status dataset <dataset name>
    list datasets
    display dataset <dataset name>
    ```

