Large language models literally do subspace projections on text to break it into contextual chunks, and then memorize the chunks. That’s how they’re defined.
Source: the paper that defined the transformer architecture and formulas for large language models, which has been cited in academic sources 85,000 times alone https://arxiv.org/abs/1706.03762
Why do I need to be polite to a computer? Who is ’?.1$#256’˜’#256$#256’? And just what is E275 DON’T BYTE OFF MORE THAN YOU CAN CHEW? All this and more on this episode of In The Program
PLEASE PLEASE PLEASE PLEASE PLEASE DO .3 <- !3˜#15’$!3˜#240
Omg that’s so evil I love it
You can look into Spotify Downloader spotdl, a Python package here: https://github.com/spotDL/spotify-downloader
It doesn’t download as you listen, but it’ll do something smart and download all the tracks of a playlist/album/etc by grabbing high-quality audio from Youtube videos (and it magically avoids dreaded music video versions) if you feed it a Spotify URL. It also puts all the metadata in the tracks automatically.
yeah it’s my favorite way to exit Vim. I post it every time I get a chance lol https://gekinzuku.com/comment/202790
ctrl-z
kill %1
I don’t have a very consistent naming theme. I’ve used various names related to music, science, and art. I have a decomissioned machine named “numbers” for example.
However, I would like to point out we have plenty more than 8 celestial bodies of interest in the solar system if you include Eris, Ceres, Pluto, Makemake, the moons of Jupiter, and more. It might not be indefinitely extendable, but may help in the short term.
I believe your “They use attention mechanisms to figure out which parts of the text are important” is just a restatement of my “break it into contextual chunks”, no?