---
title: "I trained an AI model on my phone"
date: 2026-09-13
canonical: https://solmaz.io/x/2099192852798697520/
x_url: https://x.com/onusoz/status/2099192852798697520
license: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/)
---

I trained an AI model on my phone through Telegram

Using OpenClaw running on Hugging Face infra: ML Claw

It beat DeepSeek V4 Pro on the given task while having 80 thousand times less parameters

That's right. The model, GoePT-1-20m is only 20 million parameters. It runs in the browser, on the CPU. And it beats DeepSeek V4 Pro, a 1.6 trillion parameter model, in AlmanBench

This concludes my 4 year old side project (fun fact, I created a dataset for this pre AI agents, using SpaCy, and paid 50 euros from my own pocket to rent 4090s on runpod to train a T5 variant, *years* before I joined Hugging Face. That first attempt was not very successful. This one is. My first ML adventure 🤗)

The goal of this side project was to show that you can *vibe* machine learning now, on platforms with tightly integrated GPUs, storage and compute, like Hugging Face

Including dataset creation, autoresearch and the final training run (hat tip to ML-Intern which got added to Hugging Chat the other day, which was the inspiration for ML Claw. If you like ML-Intern, you might like ML Claw as well)

Why would you want to train smaller models, while you could just use LLMs from APIs? Well, if you are a business and have a fixed use-case, it would save you a ton of money of course. It might make the difference between profitability and bankruptcy

What is the task? It is basically a language-learning aid for German. Most expats who learn German as a third language, like me, have a hard time memorizing noun genders der/die/das. Because genders are sort of randomized across nouns, like a *chair* being male and a *girl* being genderless. So one often has to make a random guess, and to make the guesses correct enough, you have to spend considerable time (months to years) learning essentially useless information

So I made a version of German that removes all that in the most optimal way possible, and then trained a tiny model to translate into that dialect. That dialect is called Alman, and this is Almanpedia, which lets you read German Wikipedia, without being bothered by der/die/das

It basically reads as if it is English, and I believe following these rules would make one achieve fluency in German much faster than going through the regular track

Btw this is how I speak German myself too. It came from a need "if I have no choice but to make mistakes, at least I should make them in a consistent, formalized way"

If you have not lived in Germany, then this is not very relatable for you, but if you did, then this will be very familiar

I have already read quite a bit of Almanpedia, and it actually works very well for me. The translator still makes mistakes in places. I have written more about that in the blog post

Interested in training your own models on Hugging Face infra and need help? Reply below, or send me a DM 🤗

ML Claw - what I used to build this: https://mlclaw.dev/
Blog post with detailed info on training: https://alman.ai/blog/introducing-goept-1-20m/
Almanpedia: https://almanpedia.org/
Try out the model: https://alman.ai/translate/
HF model: