---
description: Overview of machine learning Models in Gradient
---

# Overview

{% hint style="warning" %}
This section of the documentation covers our previous generation of Gradient. For the current version go to [Gradient Next](https://docs.paperspace.com/gradient).
{% endhint %}

The Gradient model repository is a hub for importing, managing, and deploying ML models. Gradient offers the ability to create your own **custom models** and in addition to launching pre-trained **public models**.

![Models are available within projects](https://1320806315-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-LHZRFUkajubOAmgu6Rd%2Fsync%2F693095d6e6425516da7b1a043eae75770c68bfea.png?generation=1628687230529965\&alt=media)

Gradient Models can be created in two ways: they can be 1) generated by running machine learning Experiments, or 2) imported into Gradient by uploading them directly from the Web UI or CLI. Learn more [here](create-a-model).

Gradient has a repository of Models per team, as well as a Public Models repository. If you want a Generated Model to appear in your team's Model Repository, be sure to [use the appropriate **Model Path**](../create-a-model/model-path#default-paths). Uploaded Models will automatically be placed in your team's Model Repository.

The Model Repository holds references to all uploaded Models and any Experiment-generated Models. This includes model and checkpoint files generated during an Experiment's training period as well as summary metrics associated with the model's performance, such as accuracy and loss.

**Supported Models:**

* [TensorFlow 1.x](https://www.tensorflow.org/guide/saved_model)
* ONNX (Open Neural Network Exchange)&#x20;
* Custom
