The way they explain it implies they're using this at the sampler level and not trained into the weights themselves. So unless you're using an inference library that does this, the open models will not have this kind of a watermark.

> When watermarking is used, choices are still made at random, but the source of the randomness is different. Instead of using an arbitrary random number generator to pick the next word, watermaking uses the key and a few words that come before to settle what word the model should pick.

> the watermark only changes the source of the randomness used to pick among words.