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Jev in 25 lines of Python
A parody that classifies one email by reading A/B/C logits from a local Qwen3-0.6B GGUF, then says this shape is what people mean by Jev.
- Format
- Article
- Published by
- Community
- Author
- Duarte O. Carmo
- Last verified
- Sep 2026
Works forJev for Classification
Used inJev for Software & DevTools
- Languages
- Python
- Capabilities
- Runs locallyOpen source
Highlights
- Published 22 September 2026. Loads Qwen/Qwen3-0.6B-GGUF, Q8_0, context 512, via llama-cpp-python.
- Labels are A, B, and C for Legitimate, Spam, and Phishing. It reads those token logits and softmaxes them.
- On the payroll-phish example: Legitimate 0.031, Spam 0.084, Phishing 0.885.
- The note calls the post a parody and points at OpenJev for a fuller local implementation.
Watch out
Not a TypeSafe client. One constructed email. The author says the probabilities are not always correct.
Questions about this entry
Short answers drawn from the linked project or page.
A different thing, deliberately. It loads any GGUF from Hugging Face, here Qwen3-0.6B Q8_0 through llama-cpp-python, and reads A/B/C logits after a hand-written prompt. The post says outright that it calls no API and trains nothing, then points at OpenJev and openjev-sglang for fuller local implementations.
No, and the author says so. Those are raw label-token logits put through a softmax, not RLCD-calibrated decisions. On his one constructed payroll-phish email the output is Legitimate 0.031, Spam 0.084, Phishing 0.885, and he notes in the same breath that these probabilities are not always correct.
Python 3.12 or newer, since the deps are declared as PEP 723 inline metadata: huggingface-hub, llama-cpp-python and numpy. Expect llama-cpp-python to build, a GGUF download on first run, and a 512-token context. Nothing leaves the machine, which is the actual point of the post.
The trick depends on single-token labels. It tokenizes each label string and reads index zero of the result, so a multi-token label silently scores the wrong token. It also sets logits_all=True to keep every position's logits, which grows with context length.
It reads the last-position logits for the first token of each label, subtracts the log-sum-exp to get log probabilities, then exponentiates them. For the sample email it prints Legitimate 0.031, Spam 0.084 and Phishing 0.885.
Qwen/Qwen3-0.6B-GGUF, Q8_0, with a 512-token context via llama-cpp-python and all logits kept. The script declares Python 3.12 or newer and the huggingface-hub, llama-cpp-python and numpy dependencies inline.
The post points at OpenJev, openjev-sglang and OpenJev on DiffusionGemma as fuller open implementations, and describes itself as a parody rather than a System One model.
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