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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 görüntüleme • 1 ay önce •via X (Twitter)

12 Yorum

Sakib profil fotoğrafı
Sakib1 ay önce

Language models from first principles lol

John profil fotoğrafı
John1 ay önce

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

XemplifyX profil fotoğrafı
XemplifyX1 ay önce

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

simenhegstadkrueger profil fotoğrafı
simenhegstadkrueger1 ay önce

Who cares

Rakesh Pandey profil fotoğrafı
Rakesh Pandey1 ay önce

How is this better than LLM?

Ex_Machina profil fotoğrafı
Ex_Machina1 ay önce

It can but what’s the point bruh

Jimmy Choca profil fotoğrafı
Jimmy Choca1 ay önce

but why

1Broom profil fotoğrafı
1Broom1 ay önce

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.

Pedro profil fotoğrafı
Pedro1 ay önce

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

calvin profil fotoğrafı
calvin1 ay önce

wow it's almost like sfted a base language model

Void profil fotoğrafı
Void1 ay önce

Did the same but manual

dkraj.lens profil fotoğrafı
dkraj.lens1 ay önce

Bro is making a monkey fly

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Hanako

29,894 görüntüleme • 1 ay önce

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

42,183 görüntüleme • 1 ay önce