Viewing profile — frannyg

Thanks for the detailed explanations. And the rambling as well! Pretty much every Yes and No apply. I had to understand bits of the gaps I was trying to close myself, so thanks for…

I have no freaking idea what you said in the second paragraph but I love it and it will linger in the back of my head until I understand enough to look it up. [ nodding repeatedly …

Nice. Thank you for the addition of slower memory layers. So MoE models are a bit like thinking tools running concurrently, right(?), sieving through training data on paths that ar…

Nope, this definitelly fills a few gaps, thanks. I'm still too lazy of thinking about this whole O(n) time thing even though I'm constantly wondering whether "more" or better resul…

This blew my mind a little as it feels unintuitive to do this since you wouldn't just forget what you based your previous reply on, at least not after some practice with your mind …

> the same model with give the same result Is it wrong to think of this as misleading? Don't the results for exactly the same request differ because there are multiple output strin…

So "compute" includes just having more data ... that can also be "ignored"/ "skipped" for whatever reasons (e.g. weights), ok.

Ok, thanks. My misconception kind of prohibited the insight of a potential (theoretical) assert statement, which is kind of what is meant by > if the [resulting] dataset "fits" the…

> there is no looping going on internally My thoughts after this sentence filled a huge gap I was wondering about, thanks.

Yeah, I totally forgot about training time and time of request (aaah, inference time! now I get it.) being completely different points in time because the LLM has no access to the …

I'm still figuring out "inference time" but what left me puzzled at first was that there is - to humans at least - an infinite amount of tokens that might come next, technical jarg…

That was indeed part of what I wondering about. Larger and smaller, in my beginner mind, was a difference of much recursiveness the design of the model allowed. - User request impl…

> You can't spend compute to get more detail [...] Upscaling, technically, is a thing without limits, no?

Right on. A total misconception on my part. And your answer was a nice primer before diving in to the rest of the comments. Thanks!

I wasn't able to elaborate on what I mean with "better" when I asked the question but the idea can indeed be summarized with "will an LLM increase quantity and quality of parameter…

Do they make more (recursive) queuries into their training data for breadth and depth? Or does the code limit the algorithms by design and or by constraints other than the incomple…