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AnyJev

Nokia Applied Research's package that turns any open LLM into a Jev-style decision model: typed questions are read from a single prefill, and the calibration fixes need no fine-tuning.

Category
Repos & SDKs
Format
—
Published by
Community
Author
Nokia Applied Research
Added
2026-09-25
Last verified
2026-09-25

Highlights

  • A zero-label temperature fix cuts expected calibration error from 0.240 to 0.095.
  • Auto-decidable answers at a 5% error budget rise from 7.7% to 52.0% with 100 to 500 labels.
  • The L2 head is a closed-form solve over 100 to 300 labels that finishes in seconds without gradients.
  • L2 inference costs less than one plain forward pass and adds a roughly 100 KB head at serving time.
  • Qwen3-1.7B at 18 of 28 layers reached 0.730, against Jev's published 0.727 on the same 20-question set.

Quickstart

bash
pip install "anyjev[hf]"
python -m anyjev.truncate Qwen/Qwen2.5-7B-Instruct 18 ./qwen-b18
vllm serve ./qwen-b18 --task embed \
  --override-pooler-config '{"pooling_type":"LAST","normalize":false,"softmax":false}'

Watch out

The L2 head does not transfer to another question set or model, and it needs vLLM on a GPU; fp8 quantization costs accuracy.

Reactions & coverage

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X: deepseek-v4.1-flash-jev

Reddit: A walkthrough of routing between models with Jev.

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