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A Jev-like wrapper for LLMs
Allan Riordan Boll reads one-token logprobs so a chat model returns a letter and a distribution, including on webcam frames from a vision model.
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- Allan Riordan Boll
- Last verified
- Sep 2026
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Highlights
- 25 September 2026. The prompt asks for a single letter. The request sets max completion tokens to 1, logprobs on, and top logprobs to 20.
- Each question is a separate call. A shared state prefix can be KV-cached when the backend supports it.
- He adds an attachments field for images. Jev's documented state is text or JSON.
- Webcam frames on Gemma 4 12B and an RTX 3090 ran at about 1 frame per second.
Watch out
A local experiment, not a TypeSafe client. The frame rate is his own GPU run.
Questions about this entry
Short answers drawn from the linked project or page.
A local approximation. It reads one-token logprobs from a normal chat completion, prompts for a single option letter, sets max completion tokens to 1 with logprobs on and top logprobs to 20, and reshapes that into noul, choice and score shapes. No TypeSafe service is involved.
Some of it. Generating one token avoids a long answer and is quick, but input processing still costs time on every call, so batching that Jev does in a single request is lost. A shared state prefix can be KV-cached where the backend supports it, which recovers part of it.
About 1 frame per second with three questions per frame using Gemma 4 12B on an RTX 3090, and roughly 0.2 FPS against OpenAI's endpoint with no effort to avoid a separate connection per question per frame. His pitch is that plain-text questions are easier to change than a trained vision model.
Normalized logprobs over option letters, not demonstrated calibration. The post never measures reliability of the resulting numbers, so if you intend to threshold on them you would still need to check calibration on your own data before trusting the value.
It builds a prompt with the state and lettered options, asks for the letter of the best option, and sends max_completion_tokens 1, logprobs true, and top_logprobs 20. The API returns the letter plus log probabilities for alternative tokens, which stand in for each option.
Yes. The author added an attachments field for image file paths or base64 data URLs and repeats the question per frame; with Gemma 4 12B on an RTX 3090 it runs about 1 frame per second with three questions per frame, and about 0.2 FPS against OpenAI.
It needs 2 to 20 criteria per question, and the logprobs are the model's own token probabilities rather than calibrated probabilities, so they must be normalized. The author presents it as a Jev-like wrapper, not as Jev.
Reactions & coverage
Posts, threads, and videos about this entry from around the web.
Hacker News: A one-token logprob wrapper that also classifies webcam frames.
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Hacker News: Decision Index 0.2.1 scores open reproductions of Jev on a frozen suite.
jackbravo4 pointsDecision Index 0.2.1 – 70 open reproductions of TypeSafe Jev's Decision ModelRead the thread on Hacker NewsHacker News: Pointer to a DiffusionGemma plus vLLM tutorial for a Jev-shaped classifier.
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