---
title: "Get started with Model Repository"
description: "Create a Model Repository, upload a first model from the portal, and see where a Model Endpoint picks it up."
content_type: "get-started"
product: "model-repository"
platform: "TIR"
url: "/tir/model-repository/get-started/"
---

# Get started with Model Repository

Create a Model Repository and upload your first model files to it.

## Before you begin

You need a TIR project in Delhi or Chennai and permission to create repositories in it. A Member gets that permission through a policy that allows creating and updating for the Inference service.

## Create the repository

1. In TIR, open your project. In the side menu, under **Inference**, select **Model Repository**.
2. Select **Create Repository**. The **Create Model Repository** form opens.
3. Keep the suggested **Repository Name**, which starts with `tir-model-`, or type your own. A name has up to 50 characters. It has only lowercase letters, numbers, and hyphens, starts with a letter, and ends with a letter or number.
4. Choose a **Model Type**. Pick **PyTorch** for PyTorch and Hugging Face models, **Triton** for a Triton Inference Server model repository, or **Custom** for any other format.
5. Leave **Storage Type** on **New EOS Bucket**, so that TIR creates the bucket for you.
6. Select **Create**.

TIR creates the repository and shows how to upload files to it under **Next Steps**. Close that to return to the list, where your new repository comes first and is selected.

## Upload a file

1. In the list, select the row of your repository. Its tabs open below.
2. Open the **Model Files** tab and select **Upload**.
3. To put the files at the top of the bucket, leave **Destination Bucket / Folder Path** as `/`. To use a folder, type it, such as `v1`.
4. Select up to 10 files, or drag them in, then select **Upload**.

When every file has finished, select **Ok**. The files are now on the **Model Files** tab.

## Check that it works

Your repository is in the list, and its **Model Files** tab shows the files you uploaded. If a message appears instead, see [Troubleshooting Model Repository](/tir/model-repository/troubleshooting/model-repository/).

## Use the repository from an endpoint

To serve the model, select **Deploy Model** on the repository's row. Pick a framework, then on the endpoint form select **Link with Model Repository** and choose this repository. See [Use a repository from a Model Endpoint](/tir/model-repository/how-to/use-a-repository-from-a-model-endpoint/).

## What to do next

- [Upload model files](/tir/model-repository/how-to/upload-model-files/) with the MinIO CLI, s3cmd, or the Python SDK for large files and whole folders, and for any file over 5 GB.
- [Connect to a repository with the MinIO CLI or s3cmd](/tir/model-repository/how-to/connect-to-a-repository-with-an-s3-tool/) to copy a whole model folder in one command.
- [Use a repository from an instance or notebook](/tir/model-repository/how-to/use-a-repository-from-an-instance-or-notebook/) to download the model files or save a trained model.

For help with a message, see [Troubleshooting Model Repository](/tir/model-repository/troubleshooting/model-repository/).