I am positive I know exactly how this works, I made something similar a few months back. But the problem is without generation you are extremely limited in the use cases. And while the model can't hallucinate, it can still be wrong. It just can't make up data.
Introducing System One Models and Jev
61–70 of 512 posts
Re: Introducing System One Models and Jev
#62oooooh it can play Doom!
forget LLM benchmaxxing sidequests, I'm sold on the real benchmark
Re: Introducing System One Models and Jev
#63I could see this being fantastic for classification tasks. Last year I shifted from using LLMs for bulk data classification tasks (1M transcripts) to generating embeddings and categorizing based on cosine similarity. It saved a ton of costs and time, but wasn't as accurate as LLMs. This seems like it can give me Terra-level classification ability with the cost/speed I need.
yup just joined the waiting list with a very similar use case in mind
Re: Introducing System One Models and Jev
#64Re: Introducing System One Models and Jev
#65It seems like the docs[0] are a better explanation? The comparison to llm tokens is kinda confusing. It looks like the model takes as input a state (structured text? not sure if multi-modal) and a question (as a "Choice", "Score", or "Noul") with some additional augmentations possible. Then outputs the question's answers as appropriate (e.g. a choice, accompanying probabilities, confidence). Edit: On the AI primer pa…
CEO here - that is right!
I do agree that the comparison to LLM tokens is hard to understand (also because output tokens are not comparable).
But yes, text or structured state (like a JSON with multiple pieces of text in) -> decisions out (e.g. choice maps to "match" statement, "score" maps to sorting, "noul" short for bernoulli maps to if-statements)
Re: Introducing System One Models and Jev
#66It seems like the docs[0] are a better explanation? The comparison to llm tokens is kinda confusing. It looks like the model takes as input a state (structured text? not sure if multi-modal) and a question (as a "Choice", "Score", or "Noul") with some additional augmentations possible. Then outputs the question's answers as appropriate (e.g. a choice, accompanying probabilities, confidence). Edit: On the AI primer pa…
> the model takes as input a state (structured text? not sure if multi-modal)
Input, and criteria/instructions can both be defined as structured input (JSON). This ends up being pretty powerful because the model is trained to understand structure.
e.g.: https://docs.typesafe.ai/primitives/advanced#structured-inst...
> not sure if multi-modal
just JSON... for now :)
> outputs the question's answers as appropriate
correct!
Re: Introducing System One Models and Jev
#67So in theory you could feed it incomplete text, and then ask it for the probabilities of what the next character could be?
you could, but it the model is not optimized for text
this is complex, but generating text is highly complicated and requires mode dropping to make long cohesive text
Re: Introducing System One Models and Jev
#68This puts the human even more out of the loop I'll guess?
Re: Introducing System One Models and Jev
#69Funny how it can do everything but not chat. Sort of how when I was a kid I thought of a medicine that could cure any disease except the common cold.
Re: Introducing System One Models and Jev
#70https://x.com/completeskeptic/status/2099925682726002904?s=4... The doom demo is quite cool
Link to a raw MP4 of the video, from the parent article: https://framerusercontent.com/assets/rlL7ImEbISFoYt3IJEHHfvj...
It's in the parent article under a section named "Doom" in case that asset URL ever changes.