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How to score drafts against a rubric and yes/no checks with LLM2 Jev

Rate a text on rubric criteria and get yes/no probabilities for checks like policy compliance.

Who it is forA content editor or a QA agent that reviews drafts against a fixed checklist before publishing.

The problemReviewers apply rubrics inconsistently, and generated text needs a repeatable first pass.

The outcomeRubric scores from Score and yes/no probabilities from Noul, returned as typed Python objects or JSON in one request.

What you need first

  • A local model directory supported by SGLang, Transformers, or MLX (Apple Silicon)
  • For the SGLang path: Linux with a supported NVIDIA GPU
  • Python with uv or pip, as described in the Installation guide
  • A text or image input to decide on

Steps

  1. git clone https://github.com/Yinsongxu/LLM2Jev.git
  2. cd LLM2Jev
  3. uv sync --extra sglang
  4. llm2jev-serve --backend sglang --model-path /path/to/model --served-model-name local-model --host 127.0.0.1 --port 30000 (inferred, check the README)
  5. Build one request that mixes a Score rubric and a Noul yes/no question, following the Usage guide section on the Python client (inferred, check the README)

Prompt for your agent

Using the LLM2Jev repo, call systemone.create on the running service with the draft text <draft-text>, a Score rubric for <rubric-criteria>, and a Noul yes/no check for <check-question>. Return the rubric scores and yes/no probabilities as JSON. Verify that each score is returned for every rubric item before reporting.

Caveat. The README lists Score and Noul as question types but its quick start shows only Choice, so check the Usage guide for the exact Score and Noul arguments.

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