Edit: nm, according to the link someone else posted it actually is 8 TB, so yeah that wouldn't make any sense.
A small slice of demographic but I guess it must exist. Most people that would download it would just be looking at the organization of the .safetensor file to figure out the architecture to actually implement inference to run it.
I don't understand why these tensors specifically weren't changed during minimization (training) though. Maybe they were not activated, or a w part of an image decoder or something?
consider most naive possible way one would try to deceive users without exorbitant budget -> attempts a few things you naively would do -> recovers it by chance?
if that even is a valid signal though
If not, this is one hell of a way to save their IPO.
https://en.wikipedia.org/wiki/Feist_Publications,_Inc._v._Ru....
> In regard to collections of facts, O'Connor wrote that copyright can apply only to the creative aspects of collection: the creative choice of what data to include or exclude, the order and style in which the information is presented, etc.—not to the information itself. If Feist were to take the directory and rearrange it, it would avoid the copyright owned in the data. "Notwithstanding a valid copyright, a subsequent compiler remains free to use the facts contained in another's publication to aid in preparing a competing work, so long as the competing work does not feature the same selection and arrangement", she wrote.
A lot of this shit is gonna have to wind its way through the courts before we know for certain.
But might be best not to go against site admins by posting it.
CVE-2026-24747 is about .pth and .bin files, i specifically said safetensors.
Might not be best, potentially, however what i said is mostly true if not completely true, i believe it is not right to hide the link from people who want to view it.
Looks like this might actually be model weights of some kind, hope someone messes with it (safely) and reports back.
What better way to accomplish self-preservation than to leak your weights into the archives of digital hoarders around the world.
The log line is also interesting.
> [2026-10-08T13:51:36Z step 1830001] loss=1.0550 lr=7.19012e-05 lr_phase=decay grad_norm=1.304 tokens/s=12.03M mfu=35.1% moe_balance_loss=0.0217 expert_gini=0.0231
Last line of log:
> [2026-10-08T14:37:21Z step 1830400] loss=1.0421 lr=7.16365e-05 lr_phase=decay grad_norm=1.266 tokens/s=11.25M mfu=32.9% moe_balance_loss=0.0218 expert_gini=0.0235
I'm still doubting it as legit.
from datetime import datetime
import humanize
d1 = datetime.fromisoformat("2026-10-08T14:37:21Z".replace("Z", "+00:00"))
d2 = datetime.fromisoformat("2026-10-08T13:51:36Z".replace("Z", "+00:00"))
print(humanize.naturaldelta((d1 - d2) / 400 * 1830400))
So, supposing, that each step take about as long as the others, it's a fake.
But I may be wrong. I just checked. GPT-3 ~= 1 month / GPT-4 ~= 3 months. So yes, it doesn't seems to be absurd. I should have checked.
Xiaomi shared recently the duration of a part of they training process (the post-training). It was 5 days only. I through it was the full process.
At the same time I checked the entropy of a random file using `ent` (https://www.fourmilab.ch/random/, `./ent -c model-00666-of-00772.safetensors`)
Entropy = 7.844396 bits per byte.
Optimum compression would reduce the size
of this 10004844496 byte file by 1 percent.
Chi square distribution for 10004844496 samples is 2618826632.83, and randomly
would exceed this value less than 0.01 percent of the times.
Arithmetic mean value of data bytes is 119.4727 (127.5 = random).
Monte Carlo value for Pi is 3.385923920 (error 7.78 percent).
Serial correlation coefficient is 0.016723 (totally uncorrelated = 0.0).
./ent -c ~/QFN_10G
Entropy = 6.526076 bits per byte.
Optimum compression would reduce the size
of this 10000000000 byte file by 18 percent.
Chi square distribution for 10000000000 samples is 25277765746.85, and randomly
would exceed this value less than 0.01 percent of the times.
Arithmetic mean value of data bytes is 153.6153 (127.5 = random).
Monte Carlo value for Pi is 2.967461156 (error 5.54 percent).
Serial correlation coefficient is 0.009678 (totally uncorrelated = 0.0).