I’m not knowledgeable in this area, but I wish there was a way to partition the model and stream the partitions over the input, allowing for some kind of serially processing of models that do exceed memory. Like if I could allocate 32gb of ram, and process a 500gb model but at (500/32) a 15x slower rate.
Can’t, he’s too busy with Tasker profiles.
That’s news to me, unless you’re only referring to the smaller models. Any chance you can run a model that exceeds your ram capacity yet?
Sadly, we’ll most likely see an influx of regulation right when it’s broadly accessible to the general public to run locally.
Someone needs to make a movie that kicks off with the Middle East having led the Industrial Revolution by capitalistic means of rapid production and innovation. Their need for natural resources growing, and them deciding to use military dominance to covertly get what they need in that regard. Them invading parts of an impoverished US, installing government leaders, … all of it. It would make for a hell of a movie. Kind of like the one where the Soviets won the space race.
but we don’t think over too much longer
Are… are you a seagull?
Those poor raccoons have no idea they’ve picked the wrong side. They’d have been better off adapting to moles and burrowing beneath our climate catastrophes.
Bamboo as well.
Sorry for asking, but what are the chances this gets an IOS release?
deleted by creator
Sir, that is obviously pomegranate stuffed fried eggs.
You’ll need to provide all the sites you visited immediately after each of the ones you searched. Your
originheader will give that info away freely. So if it’s in the query parameters of the URL, then you go to Facebook, it’s as easy as{k: v for k, v in (pair.split("=", 1) for pair in response.headers["origin"].split("?", 1)[-1].split("&"))}