# Quickstart

The Forefront API provides a simple interface for developers to use open-source models in their applications.

This quickstart tutorial will help get your local development environment setup, fine-tune your first model, and start using it in your application. 

If you are an experienced developer or want to just dive into using the Forefront API, check out the [API reference](https://docs.forefront.ai/api-reference/introduction).

Through this tutorial, you will learn:

1. How to install the latest SDKs
2. Some of the basic concepts of the Forefront API
3. How to fine-tune your first model
4. How to inference your model

If you run into any challenges or have questions getting started, please [contact our team](mailto:support@forefront.ai).

## 1. Create an account

First, create a [Forefront account](https://forefront.ai/sign-up) or [sign in](https://forefront.ai/sign-in). Next, go to the [API key page](https://forefront.ai/app/api-keys) and click "Generate API key". Make sure to save this somewhere safe and do not share it with anyone.

## 2. Install SDK

Forefront provides a custom Node.js and Python library which makes working with the Forefront API simple and efficient. Install the SDK in your preferred language:

{% tabs %}
{% tab title="Python" %}

```
pip install forefront
```

{% endtab %}

{% tab title="Node.js" %}

```
npm i forefront
```

{% endtab %}
{% endtabs %}

## 3. Fine-tune your first model

For the purpose of this tutorial, we'll file-tune Mistral-7B on 1,000 samples from the Open-Hermes-2.5 dataset.

Start by downloading the dataset:

{% file src="<https://3548480689-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FBq69yhv6y6bDEaRQX9GA%2Fuploads%2Fh3c0AJBRarqlvT2PkZDn%2Fopen-hermes-1000.jsonl?alt=media&token=02e60a10-1238-406a-874d-7e55f48a2705>" %}
1,000 samples from the Open-Hermes 2.5 dataset by Nous Research
{% endfile %}

{% hint style="info" %}
Before continuing, you can alternatively fine-tune via the UI in the [Forefront platform](https://forefront.ai/app/fine-tune/create).&#x20;
{% endhint %}

## 4. Inference your fine-tuned model

## Next steps

Now that you have fine-tuned your first model and ran it, it's time to explore what else is possible:

* For more detailed information on our models and the API, see our [GPT guide](https://platform.openai.com/docs/guides/text-generation).
* Visit the [OpenAI Cookbook](https://cookbook.openai.com/) for in-depth example API use-cases, as well as code snippets for common tasks.
* Wondering what OpenAI's models are capable of? Check out our library of [example prompts](https://platform.openai.com/examples).
* Want to try the API without writing any code? Start experimenting in the [Playground](https://platform.openai.com/playground).
* Keep our [usage policies](https://openai.com/policies/usage-policies) in mind as you start building.