They finetuned 1.5-3b models. This is a non-story
this post was submitted on 01 Feb 2025
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Big brain tech dude got yet another clueless take over at HackerNews etc? Here's the place to vent. Orange site, VC foolishness, all welcome.
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The headline is dumb, but the research isn't. According to the actual contents of the article, that $30 is still 27 times cheaper than what it costs OpenAI to make a similar sized model which also performs worse. That's still a big deal even if the people reporting on it made a stupid title for their article about it.
I feel like the author here doesnt know what the definition of "breakthrough" is.
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To reference a previous sidenote, DeepSeek gives corps and randos a means to shove an LLM into their shit for dirt-cheap, so I expect they're gonna blow up in popularity.
open source behaving like open source? couldn't be the evil scary chinese!
open weights is not open source. If it were, then nobody would have to work on trying to reproduce it. They could just run the build script.
unfortunately, nobody cares cos they're all thieves
OSI is gonna mandate that we call it open source now, didn't ya hear?
Non-techie requesting a laymen explanation if anyone has time!
After reading a couple of”what makes nvidias h100 chips so special” articles I’m gathering that they were supposed to have a significant amount more computational capability than their competitors (which I’m taking to mean more computations per second). So the question with deepseek and similar is something like ‘how are they able to get the same results with less computations?’ and the answer is speculated to be more efficient code/instructions for the AI model so it can make the same conclusions with less computations overall, potentially reducing the need for special jacked up cpus to run it?
Good question!
The guesses and rumours that you have got as replies makes me lean towards "apparently no one knows".
And because it's slop machines (also referred to as "AI", there is always a high probability of some sort of scam.
pretty much my take as well. I haven’t seen any actual information from a primary source, just lots of hearsay and “what we think happened” analyst shit (e.g. that analyst group in the twitter screenshot has names but no citation/links)
and doubly yep on the “everyone could just be lying” bit
The article sort of demonstrates it. Instead of needing inordinate amounts of data and memory to increase it's chance of one-shotting the countdown game. It only needs to know enough to prove itself wrong and roll the dice again.
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