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Joined 2 years ago
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Cake day: March 22nd, 2024

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  • I am late to this argument, but data center imagegen is typically batched so that many images are made in parallel. And (from the providers that aren’t idiots), the models likely use more sparsity or “tricks” to reduce compute.

    Task energy per image is waaay less than a desktop GPU. We probably burnt more energy in this thread than in an image, or a few.

    And this is getting exponentially more efficient with time, in spite of what morons like Sam Altman preach.


    There’s about a billion reasons image slop is awful, but the “energy use” one is way overblown.






  • I don’t want to leap into your throat, but have you tried a clean install of a different distro on a USB? And I mean clean; no reusing your home partition, no weird configs until you test out-of-the-box settings.

    One thing I’ve come to realize is that I have tons of cruft, workarounds, and configurations in my system that, to be blunt, screw up Nvidia + Wayland. And my install isn’t even that old.

    Hunting them all down would take so long that I mind as well clean install CachyOS.

    I haven’t bitten the bullet yet (as I just run Linux off my AMD IGP, which frees up CUDA VRAM anyway), but it’s feeling more urgent by the day.


  • It’s not so much about English as it is about writing patterns. Like others said, it has a “stilted college essay prompt” feel because that’s what instruct-finetuned LLMs are trained to do.

    Another quirk of LLMs is that they overuse specific phrases, which stems from technical issues (training on their output, training on other LLM’s output, training on human SEO junk, artifacts of whole-word tokenization, inheriting style from its own previous output as it writes the prompt, just to start).

    “Slop” is an overused term, but this is precisely what people in the LLM tinkerer/self hosting community mean by it. It’s also what the “temperature” setting you may see in some UIs is supposed to combat, though that crude an ineffective if you ask me.

    Anyway, if you stare at these LLMs long enough, you learn to see a lot of individual model’s signatures. Some of it is… hard to convey in words. But “Embodies” “landmark achievement” and such just set off alarm bells in my head, specifically for ChatGPT/Claude. If you ask an LLM to write a story, “shivers down the spine” is another phrase so common its a meme, as are specific names they tend to choose for characters.

    If you ask an LLM to write in your native language, you’d run into similar issues, though the translation should soften them some. Hence when I use Chinese open weights models, I get them to “think” in Chinese and answer in English, and get a MUCH better result.

    All this is quantifiable, by the way. Check out EQBench’s slop profiles for individual models:

    https://eqbench.com/creative_writing_longform.html

    https://eqbench.com/creative_writing.html

    And it’s best guess at inbreeding “family trees” for models:

    inbreed