Entries for September 13, 2026

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: mlclaw.dev Blog post with detailed info on training: alman.ai/blog/introducing-goept-1-20m/ Almanpedia: almanpedia.org Try out the model: alman.ai/translate/ HF model:

seize the means of computation

DeepSeek V4.1 Flash looks contaminated with public benchmarks. You can see it in the data they have put in the model card, possibly Kimi K3 as well Models that are contaminated have inverse proportionality in rank as they are tested in later benchmarks that were not present during their training Regardless of its intentionality, it's an amazing model. Its value comes not from higher quality output, but architectural innovation

I want to co-sign this, but I don’t acquiesce to blatant astroturfing I agree with @TheAhmadOsman here