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LocalLLaMA: Jev as a classifier
The r/LocalLLaMA thread arguing that Jev's constrained probabilities are ordinary zero-shot classification, and that the comparison should be other classifiers.
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- Oct 2026
Highlights
- Traictory's 25 September write-up says the post compares Jev to zero-shot and NLI classifiers, embeddings, and rerankers.
- The comparison it asks for is BTZSC, dozens of zero-shot classifiers on 22 datasets, not an LLM.
- Quoted comments call it a classifier with a marketing cycle, and say cheaper open classifiers existed first.
Watch out
This summary is Traictory's, not a full read of the thread. The BTZSC comparison is the poster's demand, not a result.
Questions about this entry
Short answers drawn from the linked project or page.
That is the poster's argument, according to the entry copy: constrained probabilities are ordinary zero-shot classification and the fair comparison is against classifiers, not chat models. The digest came from a third-party summary rather than a full read of the thread, so the argument is reported, not independently checked.
No. The digest records that the post demands a comparison with dozens of zero-shot classifiers across 22 datasets, and the caveat is explicit that this is a request for evidence, not a result. No scores are published in what the entry summarizes, and the fetched page contains no post body.
Treat them as paraphrased. The entry attributes the summary to a third party rather than a full reading of the thread, so the lines calling it a classifier with a marketing cycle and noting that cheaper open classifiers came first are secondhand. The captured source page contains only the site name.
The thread does not settle that. Its stated demand is a comparison against a large zero-shot and NLI classifier suite, embeddings and rerankers, not against an LLM. If you want a reference point, the entry copy points at the suite the poster names, and at its open reimplementations as the more useful reading.
A comparison against BTZSC, a suite of dozens of zero-shot classifiers over 22 datasets, plus embeddings and rerankers, rather than against a chat LLM.
Zero-shot and NLI classifiers, embeddings and rerankers, according to the summary, which frames constrained probabilities as ordinary zero-shot classification.
That cheaper open classifiers existed before Jev, which is a commenter's argument relayed in the summary rather than a measured result.
Reactions & coverage
Posts, threads, and videos about this entry from around the web.
Reddit: LocalLLaMA argues Jev should be compared with zero-shot classifiers, not LLMs.
LinkedIn: What multimodal decision models actually change, and what they do not.
LinkedInJevDirectory.org
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.
1 pointHow to build a Jev-style classifier with DiffusionGemma and vLLMRead the thread on Hacker NewsMore Jev resources like this
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