frannyg

24 ms·

1.
by frannyg 3y ago
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 taking the time to interpret into my question.
2.
by frannyg 3y ago
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 with a serious face and lot of resolve ]
3.
by frannyg 3y ago
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 are the same contextually, but different in terms of specifici
4.
by frannyg 3y ago
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 results could be achieved by throwing CPUs at
5.
by frannyg 3y ago
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 and memory (which I need to catch up on, I must add). I
6.
by frannyg 3y ago
> 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 strings with the same computed weights? Or do you include
7.
by frannyg 3y ago
So "compute" includes just having more data ... that can also be "ignored"/ "skipped" for whatever reasons (e.g. weights), ok.
8.
by frannyg 3y ago
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 model architecture properly, right? I have too
9.
by frannyg 3y ago
> there is no looping going on internally My thoughts after this sentence filled a huge gap I was wondering about, thanks.
10.
by frannyg 3y ago
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 training data anymore.
11.
by frannyg 3y ago
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 jargon, synonyms, lexical levels in general, so i
12.
by frannyg 3y ago
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 implies knowledge about X. - PULLING in weights for X. - Probab
13.
by frannyg 3y ago
> You can't spend compute to get more detail [...] Upscaling, technically, is a thing without limits, no?
14.
by frannyg 3y ago
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!
15.
by frannyg 3y ago
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 parameters if you give it more processing power a
16.
Ask HN: Do LLMs get "better" with more processing power and or time per request?
57 points by frannyg 3y ago|76 comments