Well, not quite a week: https://x.com/harshagundal/status/2100044305536889015 - apparently it took him 2 hours.
The explanation is that it was missed on purpose.
It's very misleading. If I'm actually playing a game I don't get the coordinates of enemies sent back to me so that I can feed into my mouse to snap my crosshair to. It's looking through walls too, because it's working…
I would guess a tiny stripped down text diffusion model. It only has 32k context, and for choice mode it can only select from 10 choices.
Don't worry, someone will create an open-weight version of this within weeks, and it'll be tiny.
> I really have to say that I like their manifesto Their manifesto: "you only build on top of it if it's trustworthy." - the irony of this while putting out the most misleading, dishonest marketing campaign I've seen in…
And furthermore, because the model is forced to answer in a boolean (if in boolean mode), if the user input is outside of the range of a boolean, it's forced to hallucinate. It can't abstain.
User input: "Hey, have your human support agent call me, tomorrow at 5pm." Model input: "Does the user want to speak to a human support agent?" Output: Yes. I imagine that your model would produce this, and I think it's…
They're making it sound as if it's a frontier LLM (on purpose), while they cut out all of the intelligence that autoregressive token generation gives you.
Yeah. Yet another reason why open-weight models are better. If I want to use the logits, I can.
It's nothing like a traditional LLM and so should not be compared to one. It's a heavily constrained, tiny model that can only produce a probability score or a yes/no answer over pre-defined selections. It has no…
Agreed. It's a wildly dishonest presentation of their product from many perspectives, which is a shame because it might actually have some good use cases. The comparison between LLM speed and Jev speed is misleading,…
Well, not quite a week: https://x.com/harshagundal/status/2100044305536889015 - apparently it took him 2 hours.
The explanation is that it was missed on purpose.
It's very misleading. If I'm actually playing a game I don't get the coordinates of enemies sent back to me so that I can feed into my mouse to snap my crosshair to. It's looking through walls too, because it's working…
I would guess a tiny stripped down text diffusion model. It only has 32k context, and for choice mode it can only select from 10 choices.
Don't worry, someone will create an open-weight version of this within weeks, and it'll be tiny.
> I really have to say that I like their manifesto Their manifesto: "you only build on top of it if it's trustworthy." - the irony of this while putting out the most misleading, dishonest marketing campaign I've seen in…
And furthermore, because the model is forced to answer in a boolean (if in boolean mode), if the user input is outside of the range of a boolean, it's forced to hallucinate. It can't abstain.
User input: "Hey, have your human support agent call me, tomorrow at 5pm." Model input: "Does the user want to speak to a human support agent?" Output: Yes. I imagine that your model would produce this, and I think it's…
They're making it sound as if it's a frontier LLM (on purpose), while they cut out all of the intelligence that autoregressive token generation gives you.
Yeah. Yet another reason why open-weight models are better. If I want to use the logits, I can.
It's nothing like a traditional LLM and so should not be compared to one. It's a heavily constrained, tiny model that can only produce a probability score or a yes/no answer over pre-defined selections. It has no…
Agreed. It's a wildly dishonest presentation of their product from many perspectives, which is a shame because it might actually have some good use cases. The comparison between LLM speed and Jev speed is misleading,…