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Apple's On-Device and Server Foundation Models

machinelearning.apple.com

151–160 of 562 posts

Re: Apple's On-Device and Server Foundation Models

#151
post #23

Earlier quoted context omitted.

Woa, good catch! Maybe they're doing better about at least being concrete about it, though I still have to side-eye "Users control their devices" (Even with root on macbooks I don't have access to everything running on it). However, the section that promises to open-source the cloud software are impressive and if true gives them more credibility than I assumed. I would still look out for places where devices they do…

> Even with root on macbooks I don't have access to everything running on it Just disable System Integrity Protection and then you do.

That has a few drawbacks, for instance you won't be able to run iOS apps anymore.

Re: Apple's On-Device and Server Foundation Models

#152
post #73

Absolutely awesome amount of content in these two pages. This was not expected. It is appreciated. I can’t wait to use the server model on a Mac to spin up my own cloud optimized for the Apple stack.

What makes you think you'll get that model?

Edit: I see they're committing to publishing the OS images running on their inference servers (https://security.apple.com/blog/private-cloud-compute/). Would be cool if that allowed people to run their own.

Re: Apple's On-Device and Server Foundation Models

#154
post #124

I hope, this could mean Apple will push the baseline of ALL Macs to have higher than 8GB of Memory. While I wish we all get 16GB M4 as baseline. Apple being Apple may only give us 12GB, and charges extra $100 for the 16GB option. It will still be a lot better than 8GB though.

It probably will change. Note that, so far, a 16GB apple device has much better usability than the equivalent on windows. This may sound biased, but the memory compression and foreground/background actions by macOS tight integration with the hardware is really good. I've never felt like I couldn't do things on smaller hardware, except (larges) LLMs.

Also when I compare with my co-workers the memory pressure is a lot less running the same software on macOS than Windows. This might have to be due to the UI framework at play.

But that said, I totally agree that Apple is doing daylight robbery with their additional RAM pricing, and the minimum on offer is laughable.

Re: Apple's On-Device and Server Foundation Models

#155

Halfway down the article contains some great charts with comparisons to other relevant models, like Mistral-7B for the on-device models, and both gpt-3.5 and 4 for the server-side models. They include data about the ratio of which outputs human graders preferred (for server side it’s better than 3.5, worse than 4). BUT, the interesting chart to me is „Human Evaluation of Output Harmfulness” which is much, much ”bette…

So it's not going to be better than other models, but it will be more censored. I guess that might be a selling point for their customer base?

Re: Apple's On-Device and Server Foundation Models

#156
post #73

Absolutely awesome amount of content in these two pages. This was not expected. It is appreciated. I can’t wait to use the server model on a Mac to spin up my own cloud optimized for the Apple stack.

Did it mentioned being able to spin up the server model locally? I must've missed that part in the article.

Re: Apple's On-Device and Server Foundation Models

#157
post #140

> We train our foundation models on licensed data, including data selected to enhance specific features, as well as publicly available data collected by our web-crawler, AppleBot. Web publishers have the option to opt out of the use of their web content for Apple Intelligence training with a data usage control. And, of course, nobody has known to opt-out by blocking AppleBot-Extended until after the announcement wher…

I hate to tell you, but I've been training a neural network on the internet for over a decade now. Specifically the one between my ears. Unfortunately, it seems to be gradually going insane.

If you're selling it to billions of people, and making big bank, I want a cut based on the parts you stole from me. If you're just using it personally, I'm cool with that.

Re: Apple's On-Device and Server Foundation Models

#158
post #119
post #100

Earlier quoted context omitted.

It cheapens the incentive greatly. And you probably aren't selling your photos to make a living.

Selling art as a way of making a living actually is a pretty recent thing. Up until not too long ago patronage was pretty much the only way to survive for artists. Maybe we will go back to that model?

Artists have been selling art for as long as we've been selling anything. Sure, some successful artists, during a few periods in history, managed to secure patronage, but those have always been the minority. Most artists have sold or traded their wares directly and your attempts to derail this discussion with inaccurate histories is neither helpful nor appropriate.

Re: Apple's On-Device and Server Foundation Models

#159
post #5

Earlier quoted context omitted.

So built on stolen data essentially.

What’s the problem with that? Reproducing copyrighted works in full is problematic obviously. But if I learned English by watching American movies, I didn’t steal the language from the movie studios, I learned it.

You're not a machine capable of acquiring that "learning" with zero effort and selling that learning to infinite buyers.

Re: Apple's On-Device and Server Foundation Models

#160
post #73

Absolutely awesome amount of content in these two pages. This was not expected. It is appreciated. I can’t wait to use the server model on a Mac to spin up my own cloud optimized for the Apple stack.

What makes you think you'll get that model? Edit: I see they're committing to publishing the OS images running on their inference servers ( https://security.apple.com/blog/private-cloud-compute/ ). Would be cool if that allowed people to run their own.

Apparently they will in a VM but it seems perhaps only security researchers?
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