---
title: "Use a repository from a Model Endpoint"
description: "Deploy a Model Repository as a Model Endpoint, or link one while you create an endpoint, so the endpoint downloads the model when it starts."
content_type: "how-to"
product: "model-repository"
platform: "TIR"
url: "/tir/model-repository/how-to/use-a-repository-from-a-model-endpoint/"
topic: ["serve-and-train"]
---

# Use a repository from a Model Endpoint

Link a repository to a [Model Endpoint](/tir/model-endpoints/), and the endpoint downloads the model when its container starts or restarts. A repository stores model files. The endpoint is the running service that answers requests. The same steps are in [Deploy a model from Model Repository](/tir/model-endpoints/how-to/deploy-a-model-from-model-repository/).

## Before you begin

- Permission to create model endpoints in the project. A Member gets it through a policy that allows creating for the Inference service. **Deploy Model** is available with the create permission.
- A repository with the model files in it, in the same project and region as the endpoint. See [Upload model files](/tir/model-repository/how-to/upload-model-files/).
- The folder the model sits in, if it is in a subfolder of the repository's bucket. If you uploaded to `v1` or `production`, give that folder.
- For a LoRA adapter on vLLM, a repository that holds the adapter's configuration file, named adapter_config.json, and a file ending in .safetensors. You also need a Hugging Face integration for the base model. See [Create a Hugging Face integration](/tir/integrations/how-to/create-a-hugging-face-integration/).

## Start from the repository

In **Model Repository**, find the repository's row and select **Deploy Model**. You can also start from **Model Endpoints** with **Create Endpoint**. Then follow the steps below.

## Link the repository

1. On **Choose Framework**, choose a framework that takes a repository: vLLM, SGLang, NVIDIA Dynamo, Custom Container, TensorRT, PyTorch, or NVIDIA Triton. Its create form opens. See [Choose a framework](/tir/model-endpoints/how-to/choose-a-framework/).
2. In **Download Type**, choose **Link with Model Repository**. TIR downloads the model from the repository when the container starts or restarts. TensorRT, PyTorch, and NVIDIA Triton offer only this choice.
3. In **Model Repository**, choose one of the project's repositories in the current region. To check what it holds, follow the link next to it. If there is none yet, create one first.
4. By default the endpoint looks for the model at the top of the repository's bucket, `s3://<bucket-name>/`, so leave **Model Path** empty for that. To use a folder, type it in **Model Path**, for example `/v1` for a version.
5. (Optional) Select **Validate** to check the path. The portal shows "Model artifacts found at given path" or "Model artifacts not found at given path".

## Choose the model type for vLLM

For a vLLM endpoint that loads from a repository, **Model Type** is required:

- **Full Model (for standard inference)** serves the model in the repository as it is.
- **PEFT Model (for LoRA-based inference)** serves a LoRA adapter on top of a base model. Type the **Base Model Name**. It must match the base model named in the repository's adapter_config.json file. Choose an **HF Token Integration** so the endpoint can download the base model.

A PEFT repository must hold adapter_config.json and a file ending in .safetensors. If the portal shows "adapter_config.json or .safetensors files not present in model repo, please check file path", check the path. If it shows "Base model name does not match with the name given in config file", correct the **Base Model Name**.

## Deploy

Finish the form and select **Deploy Endpoint**. See [Create an endpoint](/tir/model-endpoints/how-to/create-an-endpoint/).

## Check the link

The endpoint's **Overview** gives the **Model Name**, **Model Type**, and **Model Path** of a repository model. In requests, the model name is `/mnt/models` for a model from a repository and `peft-model` for a LoRA adapter.

If the endpoint cannot find the model files, check three things. The files must be on the repository's **Model Files** tab. **Model Path** must match the folder layout. The bucket name must be right. Then read the endpoint's logs. See [Troubleshooting Model Endpoints](/tir/model-endpoints/troubleshooting/model-endpoints/) and [Troubleshooting Model Repository](/tir/model-repository/troubleshooting/model-repository/).

You can delete a repository only after you delete its endpoint. See [Delete a repository](/tir/model-repository/how-to/delete-a-repository/).