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What you can do with LLM2 Jev

LLM2Jev is an open-source Python project that turns a local language model or multimodal model into a decision model that scores options, rubrics, yes/no questions, and image boxes using prefill only, with no training.

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What do you want to do?

5 use cases

  • Route support tickets to a department with probabilities

    Score a customer message against department options and get a probability for each one.

    View prompt
    Using the LLM2Jev repo, start the SGLang service with the model at <model-path>, then call client.systemone.create with the ticket text <ticket-text> and a Choice over the departments <department-list>. Return the probability for each department as JSON. Verify that the probabilities sum to about 1 before reporting.

  • Score drafts against a rubric and yes/no checks

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

    View prompt
    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.

  • Pick the next game action from an image

    Choose the best next move from a board image using a multimodal Choice question.

    View prompt
    Using the LLM2Jev repo, run the single-step Sokoban evaluator against the running SGLang service on <image-folder> with the multimodal Choice pipeline. Return the chosen action and option probabilities for each image as JSON. Verify that the output count matches the number of images before reporting.

  • Locate a target object in an image with boxes

    Get normalized bounding boxes for a named target in an image using the Box question type.

    View prompt
    Using the LLM2Jev repo, send the image <image-path> and a Box question for <target-description> to the running service. Return the normalized bounding box as JSON. Verify that coordinates fall between 0 and 1 before reporting.

  • Run decisions offline on a Mac with MLX

    Score choices in Python on Apple Silicon without a server, using the MLX backend.

    View prompt
    Using the LLM2Jev repo on Apple Silicon, run the offline Python API with the MLX backend on the model at <model-path>, asking a Choice question over <options> for the input <input-text>. Return the option probabilities as JSON. Verify that the probabilities sum to about 1 before reporting.

How to use LLM2 Jev

  1. Check you have what it needs: 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.
  2. Open Yinsongxu/LLM2Jev on GitHub and read the README and license.
  3. git clone https://github.com/Yinsongxu/LLM2Jev.git
  4. cd LLM2Jev
  5. uv sync --extra sglang
  6. source .venv/bin/activate

These steps come from our top use case, Route support tickets to a department with probabilities, which also has a copy-paste agent prompt.

LLM2 Jev alternatives · Best AI repos for Software and DevOps

About this repo

  • Who it is forDevelopers and AI agents who already run a local LLM or MLLM and need classification, scoring, or action selection without fine-tuning.
  • Why it is hot
  • LanguagePython
  • Sourcegithub.com/Yinsongxu/LLM2Jev · Back to Hot repos

Requirements

  • 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
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Questions about LLM2 Jev

What is LLM2 Jev?

LLM2Jev is an open-source Python project that turns a local language model or multimodal model into a decision model that scores options, rubrics, yes/no questions, and image boxes using prefill only, with no training. It is an open-source project on GitHub: https://github.com/Yinsongxu/LLM2Jev.

Who is LLM2 Jev for?

Developers and AI agents who already run a local LLM or MLLM and need classification, scoring, or action selection without fine-tuning.

What can I do with LLM2 Jev?

5 use cases so far, for example: Route support tickets to a department with probabilities; Score drafts against a rubric and yes/no checks; Pick the next game action from an image. Each has steps and a copy-paste agent prompt.

What do I need to run LLM2 Jev?

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.

What are alternatives to LLM2 Jev?

Other hot AI repos in Software and DevOps: Harness2, iFixAi, Orca, Diagram Design, AI Memory.

Is LLM2 Jev safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.