You can, at that will cause the same output on the same input if there is no variation in floating point rounding errors. (True if the same code is running but easy when optimizing to hit a round up/down and if the tokens are very close the output will diverge)
The point the people (or llm arguing against llms) miss is the world is not deterministic, humans are not deterministic (at least in a practical way at the human scale). And if a system is you should indeed not use an llm… Its powere is how it provides answers with messy data… If you need repeatability make a scripts / code ect.
(Note I do think if the output is for human use it’s important a human validate its useful… The llms can help brainstorm, can with some tests manage a surprising amount of code, but if you don’t validate and test the code it will be slop and maybe work for one test but not for a generic user.
You can, at that will cause the same output on the same input if there is no variation in floating point rounding errors. (True if the same code is running but easy when optimizing to hit a round up/down and if the tokens are very close the output will diverge)
There are more aspects to the randomness such as race conditions and intentionally nondeterministic tiebreaking when tokens have the same probability, apparently.
I actually think LLMs are ill suited for the vast majority of things people are currently using them for, and there are obviously the ethical problems with data centers bringing new fossil fuel power sources online, but the technology is interesting in and of itself
There are more aspects to the randomness such as race conditions and intentionally nondeterministic tiebreaking when tokens have the same probability, apparently.
Yeah, in addition to what the commenter above said about floating points and GPU calculations, LLMs are never fully deterministic.
So now you finally admit that LLMs are not truly deterministic and only near-deterministic.
I’ve told you that from the beginning, but you were too smug, to first admit that major LLM provider systems are not deterministic, and then too smug to look up what near-deterministic systems are and do some research, and barking up the wrong tree.
This is not hard stuff to understand, if you understand computing.
LOL, you clearly have no clue how floating points work in computing. What an imposter you are. Go back to your AI for more “computing” advice, Mr. “Software Engineer”.
You could at least go and verify if your AI is lying to you.
Even when proven wrong, you still don’t give up LMAO 🤣
I’m not gonna bother anymore with you, just talking to a dumb AI here.
Enjoy your “deterministic” AI and good luck in life.
You can, at that will cause the same output on the same input if there is no variation in floating point rounding errors. (True if the same code is running but easy when optimizing to hit a round up/down and if the tokens are very close the output will diverge)
The point the people (or llm arguing against llms) miss is the world is not deterministic, humans are not deterministic (at least in a practical way at the human scale). And if a system is you should indeed not use an llm… Its powere is how it provides answers with messy data… If you need repeatability make a scripts / code ect.
(Note I do think if the output is for human use it’s important a human validate its useful… The llms can help brainstorm, can with some tests manage a surprising amount of code, but if you don’t validate and test the code it will be slop and maybe work for one test but not for a generic user.
There are more aspects to the randomness such as race conditions and intentionally nondeterministic tiebreaking when tokens have the same probability, apparently.
I actually think LLMs are ill suited for the vast majority of things people are currently using them for, and there are obviously the ethical problems with data centers bringing new fossil fuel power sources online, but the technology is interesting in and of itself
Yeah, in addition to what the commenter above said about floating points and GPU calculations, LLMs are never fully deterministic.
So now you finally admit that LLMs are not truly deterministic and only near-deterministic.
I’ve told you that from the beginning, but you were too smug, to first admit that major LLM provider systems are not deterministic, and then too smug to look up what near-deterministic systems are and do some research, and barking up the wrong tree.
This is not hard stuff to understand, if you understand computing.
And yet, LLMs are not deterministic.
LOL, you clearly have no clue how floating points work in computing. What an imposter you are. Go back to your AI for more “computing” advice, Mr. “Software Engineer”.
You could at least go and verify if your AI is lying to you.
Even when proven wrong, you still don’t give up LMAO 🤣
I’m not gonna bother anymore with you, just talking to a dumb AI here.
Enjoy your “deterministic” AI and good luck in life.