> ## 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.

# Fine-tuning a model with FlexAI

> Step-by-step guide to fine-tuning a model on FlexAI

## Overview

This Quickstart tutorial will walk you through the steps needed to train a model on **FlexAI**.

By the end of this tutorial, you will have successfully trained a version of [nanoGPT](https://github.com/karpathy/nanoGPT) that has been optimized to make it readily usable. This enhanced version is available at: [https://github.com/flexaihq/nanoGPT](https://github.com/flexaihq/nanoGPT).

The process of Fine-tuning a model from a starting poiont (Base model or *Checkpoint*) a model consists of **4 main steps**:

### Prerequisites

You should have a FlexAI account. If you don't have one, you can [sign up for a free account](/signup).

### 1. Loading a *Dataset*

A **Dataset** is the collection of files that you want to use to train your model. You can upload files from your local machine or sync them in from a remote location when hosted by a third party Storage Provider (e.g., S3, GCS, R2, etc.). The Dataset files can be in any format, such as text, images, or audio.

### 2. Running a *Fine-tuning Job*

**Fine-tuning Job** is the name assigned to the FlexAI component that represents the process of executing training code on the FlexAI platform.

Creating a Fine-tuning Job requires the following 5 things:

1. **A Name** that describes your Fine-tuning Job
2. At least one **Dataset** that will be used to Fine-tune your model
3. A link to a GitHub **repository with the Fine-tuning code**
4. **A Checkpoint** name or ID, which to use to start the Fine-tuning Job from
5. The path to the *entry point script* (the file that contains the code that will be executed when the Fine-tuning Job begins)

A number of optional flags allow for the customization of a Fine-tuning Job's execution, such as:

* Specifying how many accelerators (GPUs) to use and across how many nodes.
* Setting Environment Variables and Secrets that will then be passed to the Training Runtime so they can be used by the training scripts.
* Specifying a previous *checkpoint* to resume execution from.
* Setting a specific *revision* (branch, tag, or commit) of the code repository to use.

Additionally, any number of **Hyperparameters** can be specified passed to the *entry point script*. These Hyperparameters can be used to control the behavior of the training code, such as the learning rate, batch size, or number of epochs.

### 3. Getting the Fine-tuning Job's details

Once the Fine-tuning Job is running, you can monitor its progress, view logs, evaluate its performance and resource usage, all through the FlexAI CLI, FlexAI's hosted TensorBoard, and FlexAI's Dashboard UI

### 4. Fetching the Fine-tuning Job's output

The output of a Fine-tuning Job is the result of the training process, which can include:

* Checkpoints: Saved states of the model at different points during training.
* Files written to the `/output` directory by the training scripts, such as binaries, logs, metrics, or any other files that you want to keep as a result of a successful Fine-tuning Job completion.