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"Jev cannot generate text" they say. "It's just a multiple choice classifier" they insist. FALSE! I put Jev in an autoregressive loop, with choice options being a list of potential next tokens. And it kinda works!

47,246 просмотров • 1 месяц назад •via X (Twitter)

Комментарии: 12

Фото профиля Sakib
Sakib1 месяц назад

Language models from first principles lol

Фото профиля John
John1 месяц назад

that's probably b/c post trained from normal AR LLM

Фото профиля XemplifyX
XemplifyX1 месяц назад

You've discovered to most inefficient LLM on the planet.

Фото профиля simenhegstadkrueger
simenhegstadkrueger1 месяц назад

Who cares

Фото профиля Rakesh Pandey
Rakesh Pandey1 месяц назад

How is this better than LLM?

Фото профиля Ex_Machina
Ex_Machina1 месяц назад

It can but what’s the point bruh

Фото профиля Jimmy Choca
Jimmy Choca1 месяц назад

but why

Фото профиля 1Broom
1Broom1 месяц назад

Taking the one model that refuses to write and forcing it to write, one token at a time, is a correct use of a Friday night. What is the tokens per second though. My calls land around 200ms each, which is fine for a decision and would be agony per token.

Фото профиля Pedro
Pedro1 месяц назад

Now we just need some fine tuning and RL and it will behave like a normal LLM

Фото профиля calvin
calvin1 месяц назад

wow it's almost like sfted a base language model

Фото профиля Void
Void1 месяц назад

Did the same but manual

Фото профиля dkraj.lens
dkraj.lens1 месяц назад

Bro is making a monkey fly

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Hanako

29,894 просмотров • 1 месяц назад

Jev is cool. So is it's OSS companion, Laya. The Latest Cool Thing In AI™ tends to get a lot of hype, sometimes without everyone even understanding it. So... what is this thing? Jev is an AI model that consumes input and produces output VERY differently than chat, claude, grok. The input is two things: 1) Text state to assess. Email, html, code, whatever. 2) A set of questions which will be asked about the attached state. The canonical example from TypeSafe's docs is to identify the urgency of a support ticket. We pass the model the customer text + a single noul question "is this urgent?". Jev returns a full set of JSON. This JSON is not generated with token-by-token autoregression. Jev is not trained to produce sequences of text tokens, rather to answer questions, and guarantees well-formed responses. In the example below, we see it produces a 0.99 probability (on a 0-1.0 scale) that the answer is "yes." Jev supports exactly three types of questions (seconds example in video): a) Noul: 0–1 probability that the answer to a yes/no question is "yes." b) Choice: Ask question with pre-defined set of answers. Jev chooses the best and assigns probabilities to each. c) Score: Ask question with pre-defined scale of answers. Jev produces a position on the scale. Jev computes answers for all questions in parallel, making responses super fast even for many questions in a single request. This might seem like a narrow set of capabilities, but in the right contexts leads to incredible potential. It also makes for a useful API / primitive for programming, since the outputs are... *ahem*... type-safe and predictable in structure. Jev is not going to replace LLMs for writing your code, auto-generating your docs, or being at the core of an agent harness. But Jev IS incredibly cool, and will be used to build a lot of amazing tech. Hope this helps.

Ben Dicken

40,810 просмотров • 1 месяц назад