Jev in 25 Lines of Python
Build a Jev-style decision model from any GGUF model in 25 lines: choices in, probabilities out.
About Jev in 25 Lines of Python
NobodyWho strips the mystique from decision models. Load any GGUF model with llama-cpp-python, give it lettered choices in the prompt, read the logits of the next token for each letter, and normalise them into probabilities. That is the whole trick.
Why it matters: If you pay an LLM to classify support tickets, route requests or score leads, this shows the mechanic underneath the hosted decision-model APIs: a single forward pass, no generation, no fine-tuning, and the data never leaves your machine.
The post is honest about the limits too: the probabilities are not calibrated the way a trained decision model’s are. Read it before you pick between a hosted API, a local model or 25 lines of your own.
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