---
title: "Deploy hugging-face-transformer"
description: "A Hugging Face Transformer Railway Template"
category: "AI/ML"
url: https://railway.com/deploy/z52Exi
---

# Deploy hugging-face-transformer

A Hugging Face Transformer Railway Template

**[Deploy hugging-face-transformer on Railway](https://railway.com/template/z52Exi)**

- **Creator:** Yoeven D Khemlani's Projects
- **Category:** AI/ML
- **Total deploys:** 34

## Template content

### transformer-fastapi

- **Source:** https://github.com/JigsawStack/huggingface-transformers-railway-template
- **Public domain:** Yes

## Documentation

# Hugging Face Transformer Example

This example starts up a [FastAPI](https://fastapi.tiangolo.com/) server which runs the [Hugging Face Transformers](https://huggingface.co/docs/transformers/en/index).

This example runs an embedding model on the CPU which works great with railway as resources can scale per requests.

Note: This won't work for GPU work flows and will crash.

## ⬆️ Deploy

[![Deploy on Railway](https://railway.com/button.svg)](https://railway.com/template/z52Exi?referralCode=66iek4)

or

Install the [Railway CLI](https://docs.railway.com/guides/cli), then run `railway up`

## ✨ Features

- Transformers
- FastAPI
- Hypercorn
- Python 3.11

Uses [Nixpacks](https://nixpacks.com/docs/providers/python) to deploy on railway which runs Python 3.11 by default.

## 💁‍♀️ How to use locally

- Clone locally and install packages with pip using `pip install -r requirements.txt`
- Run locally using `hypercorn main:app --reload`

## 🧩 Background

The team at [JigsawStack](https://jigsawstack.com) is launching an embedding model and we're experimenting with different infrastructure for scalable and affordable CPU usage. Check out the [embedding model here](https://jigsawstack.com/blog/introducing-multimodal-multilingual-embedding-model-for-images-audio-and-pdfs-in-alpha)

## Common issues

- When deployed on Railway, your instance region might be deployed to a metal region which is currently in BETA and seem to be a lot slower than non-beta regions. Switch to US West (Oregon, USA) for the best performance on testing. Metal regions don't have volume attachments which could be the issue for caching data.
- You can attach a volume if you switching models in a single instance, this would allow for better caching and faster switches
- If you need a specific python version, you can set `NIXPACKS_PYTHON_VERSION` in the variables tab to the desired version

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