I mean the derivative of a constant is 0. So if all of the original weights are considered constants, then computing their gradients is trivial, since they’re just zero.
You’re assuming a lot more intercompany coordination than would exist. Even though it’s research by Microsoft labs, the researchers themselves are to a large extent autonomous and also narrow experts in their fields.
This process involves low rank approximations -> Lora is a namey sounding term that uses characters from low and rank -> call it LoRA in the paper. That’s all there was to it. Probably didn’t even know the other lora existed.
> But this is smaller than the alternative of a model which contains the large matrix of original weights, and an equally large matrix of alterations.
It's actually larger. If you just have two equally large matrices of the same dimension, one original, and one of "altercations"... then you can just add them together.
> Why is fine-tuning done with separate alterations, rather than by mutating the original weights?
Then you'd have to compute the gradients for the whole network, which is very expensive when the model has 7b, 65b, 165b parameters. The intent is to make that cheaper by only computing gradients for a low rank representation of the change in the weight matrix from training.
Random projects work well in high dimensional spaces, they’re cheap, easy, and require no understanding of the initial space. Part of the point of Lora is efficiency, after all!
It’s actually completely different. What you linked is about zero shot learning by adjusting the prompt, vs Lora which is about actually fine tuning the weights of the model.
Per the original paper, empirically it’s been found that neural network weights often have low intrinsic rank. It follows, then, that the change in the weights as you train also have low intrinsic rank, which means that you should be able represent them with a lower rank matrix.
I suspect it’s not that similar. The intuition behind LoRA is more true the higher the rank of the weights of the model. Even the smallest LLMs have considerably higher rank weights than Stable Diffusion. They are large, after all.
Kinda, it helps that you can do a proper pre-infusion phase, but in the end the bottleneck is human - there's no way you can get anywhere close to espresso pressure (not just 9 bars, anywhere near 9 bars) with your arm. You need mechanical advantage at least.
The Prismo doesn't really increase the pressure in the chamber you can have by that much.
They function as headphones. The main issue, for worse or sometimes for better, is that you need an iPhone to update the firmware. Apple's Quick pairing also won't work, but Android doesn't really have an equivalent anyway.
I mean hearing protection isn't a binary thing. I doubt they would pass hearing protection certifications for things that they're normally used for, like heavy construction or whatnot, but it'd be hard to imagine sticking something in your ear wouldn't offer hearing protection, not even considering the ANC.
It's definitely not a toaster oven. The fan is important - there's a reason CPU radiators have fans on them, and it's not because they don't do anything.
You can argue it's just a convection oven with a faster fan, but that doesn't make it a "meme".
They're also, like, very much not expensive at this point. It's a very commoditized, competitive market, and you can pick them up for <$50.