I missed the hypewave so can't say a lot about Jev, but the double standards are entertaining:

About Jev:

> We didn't train a model with Reinforcement Learning for Calibrated Decisions (RLCD) to calibrate the decisions and probabilities (even though they are not always correct).

Only 99% correctness! Borderline unusable!

About their model:

> It classifies: it gets a prompt with choices and outputs probabilities.

You want numbers, it gives you numbers! What more could you want?

a hile ago (when big providers still provided logprobs) i created a VS Code highlighter that visualizes unsure tokens.

Since most chat models want to answer with a human-readable message i think their logprobs are not as meaningful. It would be interesting to see if one choice is like "correct" and if the model wants to choose it more often, cause it might not answer the question but to prose to the user.

You can also go beyond Jev. Qwen 3.5 0.8B is fantastic at basic image classification/question answering (including OCR elements) also. Though rather than looking at logits, I get it to output a structured JSON object and it does simple object classification tasks on a Mac at under 500ms a pop (I forget how far, but I think it's like ~250ms) with good accuracy (depending on task).

kinda feel like the "this is a parody blog post, see these links for better/more complete open implementations of Jev..."

is a legal cya a la "Nathan For You" 's Dumb Starbucks

I've built something similar, i am hosting it.

You can test Jev like model at 26B parameter count here (built few weeks ago): https://gambler-relay-us-west1.leo-fish.ts.net/demo (might not stay up for long)

Typesafe compatible API

This is just running on old hardware.

What I don’t understand is, why would you not want “reasoning” in a classifier?

Speed and cost are obvious reasons, but isn’t this a tradeoff?

to be fair a Jev architecture would be better optimized for this particular workflow than an LLM.

    <think>\n\n</think>

but letting an LLM think would trade latency and performance for significant reliability above that of Jev.

Pretty interesting how a simple example like this makes the idea so easy to understand.

> Email: {email}\n\n{options}<|im_end|>

Why do I have to feed my e-mail into the model?

> "25 lines of python" > "import Solution"

ok