---
url: /docs/get-started.md
description: >-
  Learn how to install and use the Kit CLI to package, version, and share AI/ML
  models using ModelKits. Follow our step-by-step guide for setup and
  deployment.
---

# KitOps Getting Started Tutorial

KitOps is the open-source CLI tool for packaging and sharing complete AI/ML projects using the [ModelKit](../modelkit/intro.md) format.

In this guide, you'll:

1. Unpack and inspect a sample ModelKit
2. Package your own model and data
3. Push it to a remote registry for collaboration or deployment

> Prefer to use KitOps with your favorite MLOps or other tool? [Check out our integrations](../integrations/integrations.md)

## Prerequisites

* [Install the Kit CLI](../cli/installation.md)
* Verify it by typing `kit version` in a new terminal
  * If you get an error check your PATH
* Create and navigate to a new folder (e.g., `KitStart`)

## Step 1: Log In to a Registry

You can use any OCI-compatible registry. We’ll use Jozu Hub for this example:

```sh
kit login jozu.ml
```

Use the email and password you signed up to your registry with.

Trouble? See the [kit login docs](../cli/cli-reference.md#kit-login).

## Step 2: Unpack a Sample ModelKit

:::tip
If you already have a model or dataset on your machine navigate to the directory where the files are and run `kit init .` in your terminal to build a Kitfile automatically.
:::

We’ll pull and unpack a fine-tuned Llama 3 model:

```sh
kit unpack jozu.ml/jozu-quickstarts/fine-tuning:latest
```

This unpacks all files to the current directory:

```sh
.
├── Kitfile
├── README.md
├── llama3-8b-8B-instruct-q4_0.gguf
├── lora-adapter.gguf
└── training-data.txt
```

## Step 3: Pack your ModelKit

Use the [kit pack command](../cli/cli-reference.md#kit-pack):

```sh
// Replace <your-name> with your Jozu.ml user
// [!code word:/your-username]
kit pack . -t jozu.ml/your-username/finetune:latest
```

This saves the ModelKit locally under the `latest` tag.

:::tip Packing as ModelPack format
To create a [ModelPack](https://github.com/modelpack/model-spec)-formatted artifact instead, add the `--use-model-pack` flag:

```sh
kit pack . --use-model-pack -t jozu.ml/your-username/finetune:latest
```

All Kit commands work with both ModelKit and ModelPack formats.
:::

Verify your packed model:

```sh
kit list
```

## Step 4: Push to a Remote Registry

Now push your ModelKit to share it:

```sh
// Replace <your-name> with your Jozu.ml user
// [!code word:/your-username]
kit push jozu.ml/your-username/finetune:latest
```

💡 If you see an error, check that your target repository exists and that you have permission to push.

## Next Steps

If you'd like to learn more about using Kit, try our [Next Steps with Kit](../next-steps/) document that covers:

* Creating a container or Kubernetes deployment from a ModelKit
* Signing your ModeKit
* Making your own Kitfile
* The power of `unpack`
* Tagging ModelKits
* Keeping your registry tidy