Nokia Open-Sources AnyJev: A Training-Free Layer That Turns Any Open LLM Into a Calibrated Decision Model
Nokia’s applied research team has open-sourced AnyJev, a Python library that turns an open LLM into a decision model. It needs no training. It targets a common production job: picking one answer from a fixed set instead of writing a sentence. Is it deployable? Yes, it installs from PyPI, ships under Apache-2.0, and has transformers and vLLM backends with shared-prefix scoring. What is AnyJev? AnyJev borrows its interface from Jev. Jev is the System One decision model that TypeS
Get smart on it
AnyJev is an open-source Python library that converts open language models into decision models without requiring training. Instead of generating text, it picks one answer from a fixed set of options and provides a probability score based on the model's token distribution. The library addresses two key problems with directly reading model scores: answers can change when options are reordered, and probabilities are often poorly calibrated. AnyJev solves these issues through two levels of correction: L0 uses cyclic shifts of option order and batch prior correction to remove position and label bias, while L1 adds temperature scaling for better calibration.
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