---
title: "Deploy a model from Model Repository"
description: "Deploy your own or fine-tuned model from a Model Repository as a Model Endpoint, with a model path, a validation check, and a LoRA adapter option for vLLM."
content_type: "how-to"
product: "model-endpoints"
platform: "TIR"
url: "/tir/model-endpoints/how-to/deploy-a-model-from-model-repository/"
topic: ["create"]
---

# Deploy a model from Model Repository

Deploy a model whose files are in a [Model Repository](/tir/model-repository/). The endpoint downloads the model from the repository when its container starts or restarts. Use this for a fine-tuned model or a LoRA adapter. The same steps are in [Use a repository from a Model Endpoint](/tir/model-repository/how-to/use-a-repository-from-a-model-endpoint/).

## Before you begin

- Permission to create model endpoints in the project. A Member needs a policy that grants create permission for the Inference service.
- 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 folder rather than at the top 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 the endpoint

In **Model Endpoints**, select **Create Endpoint**, or select **Deploy Model** on a repository's row in **Model Repository**. Then follow the steps below, and link the repository on the form.

## Link the repository

1. Choose a framework that offers **Link with Model Repository**, such as vLLM, SGLang, NVIDIA Dynamo, Custom Container, TensorRT, PyTorch, or NVIDIA Triton. 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>/`. To use a folder, type it in **Model Path**, for example `/v1` for a version. Leave **Model Path** empty for the top of the bucket.
5. (Optional) Select **Validate** to check that TIR finds model files at the 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 TIR reports "adapter_config.json or .safetensors files not present in model repo, please check file path", check the path. If it reports "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

When the endpoint is **Running**, its **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, the folder is empty or missing. Check three things: the files are on the repository's **Model Files** tab, **Model Path** matches the folder layout, and the bucket name is 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/).

The repository and its files stay in place if you delete the endpoint later.