Built with QVAC: a translation demo running TranslatePsy-AfriSLM on your phone

TranslatePsy-AfriSLM is our translation model for 19 African languages, in sizes small enough to run on a device somebody already owns. We built a demo for anyone to try freely.

Translation is where QVAC and Tether have put real research effort, because it is the door everything else goes through. A school notice, a farming leaflet, a health instruction, a form: none of them reach a person if they exist only in a language that person does not read. Where language support does exist it usually sits behind a cloud service, which needs a connection and a payment method, and across much of Africa both are the exception.

What the demo does

• Paste or type text and translate it between English and any of the 19 languages, in either direction.
• Photograph a page and it comes back translated.
• One African language straight into another, Swahili into Oromo, with no English in the middle.
• Works with the network off once the model is on disk. No account, no API key, nothing to pay.
• It picks the model for your machine. The model comes in six versions and the demo does not ask you to choose: it looks at the machine, takes the strongest size that will run well on it, and uses one already on the disk if there is one.

The scan feature: combining AfriSLM with a vision model

TranslatePsy-AfriSLM translates text. Pair it with a QVAC vision model in the same app and you get the scan feature: the vision model reads the words off the photograph, you can correct them, and TranslatePsy-AfriSLM translates them. Both models run locally, one after the other.

Where it fits

Good for:

• Reading a page in a language you do not read, on a device with no signal.
• School notices, timetables, forms, leaflets, a message from a relative.
• Getting the gist of something quickly, for free, without uploading it.

Less good for:

• Anything with important implications. 
• Running a service on. It is an example to clone and learn from.

Reminder: Always perform adequate testing before deploying in any critical AI applications.

Recommended hardware

The phone column is the reason these sizes exist: TranslatePsy-AfriSLM is published as GGUF for on-device deployment and the release notes it as running on an ordinary phone with no GPU and no connection. Both columns are guidance from the download size against the memory those devices have, rather than a benchmark run on each one.

This particular demo is a local web app, so it runs on a laptop or a desktop: macOS 14+, Windows 10+ or Linux, with Node.js 22.17 or newer. Apple Silicon uses Metal, a Vulkan GPU works too, and CPU is slower but fine at the smaller sizes. Putting the same model into a mobile app is a separate build, and the model sizes above are what make it possible.

The demo measures your machine and picks a size, so the table is background rather than something to work out. Not sure it will run at all? npx -y @qvac/cli doctor.

Get it

Open http://localhost:3065. There is nothing to install: the app resolves the SDK from the qvac CLI already on your PATH. It measures the machine, downloads a size, and asks whether you also want the vision model for scanning paper.

QVAC is open source and free, and the docs are at docs.qvac.tether.io. If you build something with it, star the repo and show us.

A note on what this is

This is an illustrative example. QVAC and Tether do not sell it as a product, and it shouldn't be seen as a final product. It ships as-is, with no support and no warranty. You alone are responsible for how you use it.

Combining TranslatePsy-AfriSLM with a vision model to read photographed pages is something we put together for this demo, and it has not been extensively tested by the research team. Treat the scan path as a demonstration of what the two models can do together.

TranslatePsy-AfriSLM's weights are Apache 2.0, released for research and educational purposes. Its synthetic training data carries a different licence, CC-BY-NC 4.0, and the two should never be quoted as one. The model covers 19 fine-tuned languages, with 8 more reported as a transfer result rather than a support claim. The paper is a forthcoming EMNLP 2026 submission, live now as a preprint at arxiv.org/abs/2608.18655.