> ## Documentation Index > Fetch the complete documentation index at: https://docs.flex.ai/llms.txt > Use this file to discover all available pages before exploring further. # Creating a Fine-tuning Job > Create and configure a new fine-tuning job on FlexAI With a Dataset available on your FlexAI account, you can now create a Fine-tuning Job that will use it. ## The Model's repository For this tutorial we will use the [**FlexAI fork of the nanoGPT repository**](https://github.com/flexaihq/nanogpt), originally created by [Andrej Karpathy](https://github.com/karpathy). A Fine-tuning Job requires at least a **Name**, a link to a **GitHub repository** where its code resides, and the ***path to the entry point script*** that will initiate the Workload. In addition, the *entry point script* can be followed by any arguments required, such as configuration files or Hyperparameters. ### Entry Point script arguments The entry point script path for this quickstart tutorial is [`./train.py`](https://github.com/flexaihq/nanoGPT/blob/main/train.py), and it expects the following arguments: * `config/train_shakespeare_char.py`: A configuration file, which contains the default Workload Parameters. * `--dataset_dir`: The path within the `/input` directory of the Workload Runtime where the Dataset files are located. * `--out_dir`: The output directory, which will be mounted into the Workload Runtime as `/output-checkpoint`. * `--max_iters`: The maximum number of iterations to run the Workload script for (optional). These include any **Environment Settings** and **Hyperparameters** the entry point script may require. For this tutorial: | Parameter | Type | Description | | ---------------------------------- | ------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `config/train_shakespeare_char.py` | Environment Setting | A positional argument pointing to a configuration file used by nanoGPT's `train.py` script to set the default Workload Parameters | | `--out_dir=/output-checkpoint` | Environment Setting | The output directory where the Workload script will write checkpoint files. In order to take advantage of FlexAI's Managed Checkpoints feature, this **`should always be /output-checkpoint`** | | `--max_iters=1500` | Hyperparameter | The maximum number of iterations to run the Workload script for. This is an optional hyperparameter that can be used to tweak the Workload execution | ## Starting a new Fine-tuning Job The **Start a new Fine-tuning job** form consists of a set of required and optional fields that you can use to customize your deployment. ### To open the **Start a new Fine-tuning job** form Either: * Follow the direct link to the "Start a new Fine-tuning Job" page. Or Navigate to the **Fine-tuning** section from either the navigation bar or the card on the home page. Select the **New** button to display the creation form. A drawer menu with the creation form will be displayed. ### Required Fields * **Name**: Your Fine-tuning Job name. Should follow the [resource naming conventions](/best-practices/resource-naming-conventions/). * **Repository URL**: The URL of the Git repository containing your Fine-tuning code.\`. * **Entry Point**: The path to the entry point script in your repository that will initiate the Fine-tuning Job. * The *entry point script* can be followed by any arguments you want to pass to it, such as configurations and Hyperparameters. **Value**: `train.py config/train_shakespeare_char.py --dataset_dir=my_dataset --out_dir=/output-checkpoint --max_iters=1500`. ### Other fields * **Repository Revision**: The Git revision (branch, tag, or commit) you want to use for this Fine-tuning Job. The `main` branch will be used by default. * **Node Count**: The number of nodes you want to use for this Fine-tuning Job. Defaults to `1`. * This will determine the amount of Accelerators you will have available for your Fine-tuning Job: * 1 node will allow you to use up to 8 Accelerators. * Using more than 1 node will make all 8 Accelerators per Node available to your Fine-tuning Job. * **Accelerator Count**: The number of Accelerators you want to use for this Fine-tuning Job. Must follow the logic described above. Defaults to `1`. * **Datasets**: Can be selected from a dropdown list of the datasets you want to use for this Fine-tuning Job. You can add multiple datasets as well as specify the mount path within the Fine-tuning Runtime (they will be mounted under `/input`). You can read more about this in the [Pushing a Dataset guide](/core-services/training/quickstart/uploading-a-dataset/). Don't forget to select the "Add" button after picking a Dataset, otherwise it won't be added to the Training Job. * **Environment Variables & Secrets**: Add any environment variables you want to set for this Fine-tuning Job. These will be available to your Fine-tuning code as environment variables within the Training Runtime. * You can also reference **Secrets**, which will be securely injected into the Fine-tuning Job's Runtime. * **Cluster**: The cluster where the Fine-tuning workload will run on. It can be selected from a dropdown list of available clusters in your FlexAI account. A default cluster will be automatically selected for you if none is specified. ### Form Values | Field Name | Value | | ----------------------- | ------------------------------------------------------------------------------------------------------------------ | | **Name** | `nanoGPT-flexai-console` | | **Repository URL** | `https://github.com/flexaihq/nanogpt` | | **Repository Revision** | `main` | | **Node Count** | `1` | | **Accelerator Count** | `1` | | **Entry Point** | `train.py config/train_shakespeare_char.py --dataset_dir=my_dataset --out_dir=/output-checkpoint --max_iters=1500` | | **Datasets** | Dataset: `nanoGPT-dataset` (from the CLI quickstart),
Mount Directory: `my_dataset` | | **Cluster** | *Your organization's designated cluster* | ### Entry Point script arguments The entry point script for this Fine-tuning Job is `train.py`, and it expects the following arguments: * `config/train_shakespeare_char.py`:A configuration file, which contains the default Fine-tuning Parameters. * `--dataset_dir`: The path within the `/input` directory of the Fine-tuning Runtime where the Dataset files are located. * `--out_dir`: The output directory, which will be mounted into the Fine-tuning Runtime as `/output-checkpoint`. * `--max_iters`: The maximum number of iterations to run the Fine-tuning script for (optional). These include any **Environment Settings** and **Hyperparameters** the Fine-tuning script may require. For this tutorial: | Parameter | Type | Description | | ---------------------------------- | ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `config/train_shakespeare_char.py` | Environment Setting | A positional argument pointing to a configuration file used by nanoGPT's `train.py` script to set the default Fine-tuning Parameters | | `--out_dir=/output-checkpoint` | Environment Setting | The output directory where the Fine-tuning script will write checkpoint files. In order to take advantage of FlexAI's Managed Checkpoints feature, this **`should always be /output-checkpoint`** | | `--max_iters=1500` | Hyperparameter | The maximum number of iterations to run the Fine-tuning script for. This is an optional hyperparameter that can be used to tweak the Fine-tuning Job execution | *** After filling out the form, select the **Submit** button to start the Fine-tuning Job. You should get a confirmation message indicating that the Fine-tuning Job creation process has been initiated successfully. The **Start a new training job** form will close and you will be redirected to the Fine-tuning Jobs list page, where you can see your newly created Fine-tuning Job in the list. *** Considering the minimum required elements for the creation of a Fine-tuning Job, the following command will initiate its creation and start it running immediately: ```bash theme={null} flexai training run quickstart-fine-tuning-job \ --dataset nanoGPT-dataset=my_dataset \ --repository-url https://github.com/flexaihq/nanogpt \ --checkpoint a1b18a7f-9b85-4c74-91a9-6aca526e8ce4 \ -- train.py config/train_shakespeare_char.py --dataset_dir=my_dataset --out_dir=/output-checkpoint --max_iters=1500 ``` #### Arguments | FlexAI command Argument | Value | Description | | ------------------------ | ---------------------------- | ------------------------------- | | **Fine-tuning Job Name** | `quickstart-fine-tuning-job` | The name of the Fine-tuning Job | #### Flags | Flag | Value | Description | | ---------------------- | ------------------------------------- | -------------------------------------------------------------------------------------------------- | | **Dataset** | `my_dataset=nanoGPT-dataset` | The mount path followed by the Dataset name. The dataset will be accessible at `/input/my_dataset` | | **Repository URL** | `https://github.com/flexaihq/nanogpt` | The URL of the GitHub repository containing the workload's code | | **Entry Point Script** | `train.py` | The path of entry point Fine-tuning script as defined by the repository | #### Entry Point script arguments These include any **Environment Settings** and **Hyperparameters** the entry point script may require. Keep in mind that these are specific to the code you're running: | Entry point script argument | Type | Description | | ---------------------------------- | ------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `config/train_shakespeare_char.py` | Environment Setting | A positional argument pointing to a configuration file used by nanoGPT's `train.py` script to set the default runtime Parameters | | `--out_dir=/output-checkpoint` | Environment Setting | The output directory where the script will write checkpoint files. In order to take advantage of FlexAI's Managed Checkpoints feature, this **`should always be /output-checkpoint`** | | `--max_iters=1500` | Hyperparameter | The maximum number of iterations to run. This is an optional hyperparameter that can be used to tweak the Workload execution | ## Up next Next you'll learn how to get a Fine-tuning Job's details and monitor its progress.