A post went up on r/LocalLLaMA this morning arguing that AutoTrust’s models reached Hugging Face’s trending page on farmed likes. The author clicked through the accounts that had liked them and found profiles that looked, in their words, awfully realistic.
We have a reason to care beyond the gossip. Our own news radar reads the Hugging Face trending list every day, and it picked up five AutoTrust models this month.
The numbers on the organisation
AutoTrust has 15 public models with 14,608 likes between them. The largest, a 27 billion parameter vision model created on 30 September, has 7,721 likes. For scale, Qwen-Image-2.1, an official release from one of the biggest labs in the field that dominated these notes for a fortnight, has 3,382.
That alone proves nothing. Popular things get liked. So we looked at who did the liking.
What we measured
We took a random sample of 40 accounts from the likers of that 27B model and 40 from the likers of Qwen-Image-2.1 as a control, and pulled each account’s public profile from the Hugging Face API. We are publishing the totals only and no account names.
The first measure we tried was empty profiles: accounts with no models, no datasets, no spaces and no followers. It showed nothing useful. Nine of the AutoTrust sample were empty against twelve of the control. Lots of real people use Hugging Face only to download things and press like, so an empty profile is normal, and we would have been wrong to treat it as a tell.
The second measure was account age, and it was not close.
| sample of 40 likers | created Aug to Oct 2026 | created before August 2026 |
|---|---|---|
| AutoTrust 27B model | 40 | 0 |
| Qwen-Image-2.1 (control) | 11 | 29 |
Every account in the AutoTrust sample was created on or after 14 August. Nearly three quarters of the control predate August, with the oldest going back to 2021, which is what an audience accumulated over years looks like.
Forty is a sample, not a census, and an account being new is not proof of anything about the person behind it. What the numbers support is narrower: the audience behind those likes does not look like the audience behind a model people actually adopted, and it is consistent with what the original post claimed. Who did it, and why, is not something a profile page can tell anyone.
What this did to our own radar
The trending feed brought five AutoTrust models into our daily scan. The filter that decides what we actually read passed none of them.
We would like to claim the filter caught them. It almost certainly did not. It scores items for relevance to local image and video work, and a 27B decision model that needs 80 GB of memory scores low on that whatever its likes say. It got the right answer for an unrelated reason, and we did not notice either way until this morning.
The honest lesson is about us. We have quoted like counts in these notes as a rough sign of interest, usually beside a download figure or a check of our own. After today a like count on its own is not evidence of anything, and we will treat it the way we treat a press release: a claim to check, not a measurement.
What to use instead
If you pick models from the trending page, the evidence that is hard to fake is the kind that costs somebody effort. Somebody independent running the model and publishing numbers. Issues and discussions with real technical content in them. Quantisations and node packs made by other people, because nobody builds tooling for a model they have not used. A licence file and a model card that answer obvious questions.
And, for anything you depend on, running it yourself. That remains the only measurement in this hobby that nobody else can inflate.
A daily note from our news radar. The week gets the full treatment in the DIY AI Brief every Monday.