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What you can do with One Jev

OneJev is a Python package and set of Qwen-based multimodal models that return calibrated probabilities for typed questions about screenshots, photos, video and text in one forward pass.

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OmniJev/OneJev on GitHub

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What do you want to do?

4 use cases

  • Pick the next UI action from a screenshot

    Ask a local model whether a task is done, what to click next and how far along it is.

    View prompt
    Using the OneJev repo at https://github.com/OmniJev/OneJev, start the server with `qev serve --model OmniJev/OneJev1.6-4B --port 8000`, then send <screenshot path> with the task <task text> through the Python Client example. Return the done probability, the next-action probabilities and the progress score as JSON. Verify that the probabilities for the next-action question sum to about 1 before you act on them.

  • Run the One Jev API and playground in Docker

    Start a Compose stack that detects your hardware and serves the API and playground on port 8000.

    View prompt
    Using the OneJev repo at https://github.com/OmniJev/OneJev, run `cp docker/.env.example docker/.env` and `./docker/start.sh`, then confirm the service answers at http://localhost:8000. Report which backend was selected and the status of the running containers. Verify that the playground loads at http://localhost:8000 before reporting success.

  • Run a small One Jev model on a laptop with llama.cpp

    Serve the 0.8B GGUF build for text and image questions without an NVIDIA GPU.

    View prompt
    Using the OneJev repo at https://github.com/OmniJev/OneJev, install llama.cpp, then run `qev serve --gguf mradermacher/OneJev1.6-0.8B-GGUF:Q8_0 --port 8000`. Send one image with a yes/no question and return the noul probability. Verify that the request uses an image and not a video before reporting the result.

  • Fine-tune One Jev on your own labeled questions

    Train a checkpoint on your own media and typed questions using the repo's config files.

    View prompt
    Using the OneJev repo at https://github.com/OmniJev/OneJev, install with `pip install -e ".[train]"` and run the 100-example demo unpack step. Report the path to train/configs/onejev_4b_full.yaml and the number of lines in data/onejev/train.jsonl. Verify that data/onejev/media/ exists before you start training.

How to use One Jev

  1. Check you have what it needs: Python with pip; An NVIDIA GPU for the PyTorch backend, or llama.cpp installed for the GGUF backend; Docker, only if you use the Docker option.
  2. Open OmniJev/OneJev on GitHub and read the README and license.
  3. Run pip install "qev[torch] @ git+https://github.com/OmniJev/OneJev.git"
  4. Run qev serve --model OmniJev/OneJev1.6-4B --port 8000 and leave it running
  5. In another terminal, save your screen as screenshot.png and run the Python example from the README with your task text in state
  6. Read r.answers["done"].noul for the probability of yes, r.answers["next"].probabilities for the action options and r.answers["progress"].score for the expected level

These steps come from our top use case, Pick the next UI action from a screenshot, which also has a copy-paste agent prompt.

One Jev alternatives · Best AI repos for Software and DevOps

About this repo

  • Who it is forDevelopers and AI agent builders who need a local model to judge screens, photos, video or text and return probabilities for yes/no, choice and score questions.
  • Why it is hot
  • LanguagePython
  • Sourcegithub.com/OmniJev/OneJev · Back to Hot repos

Requirements

  • Python with pip
  • An NVIDIA GPU for the PyTorch backend, or llama.cpp installed for the GGUF backend
  • Docker, only if you use the Docker option
  • A Hugging Face token only for gated or private repos (set HF_TOKEN in docker/.env for Docker)
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Questions about One Jev

What is One Jev?

OneJev is a Python package and set of Qwen-based multimodal models that return calibrated probabilities for typed questions about screenshots, photos, video and text in one forward pass. It is an open-source project on GitHub: https://github.com/OmniJev/OneJev.

Who is One Jev for?

Developers and AI agent builders who need a local model to judge screens, photos, video or text and return probabilities for yes/no, choice and score questions.

What can I do with One Jev?

4 use cases so far, for example: Pick the next UI action from a screenshot; Run the One Jev API and playground in Docker; Run a small One Jev model on a laptop with llama.cpp. Each has steps and a copy-paste agent prompt.

What do I need to run One Jev?

Python with pip; An NVIDIA GPU for the PyTorch backend, or llama.cpp installed for the GGUF backend; Docker, only if you use the Docker option; A Hugging Face token only for gated or private repos (set HF_TOKEN in docker/.env for Docker).

What are alternatives to One Jev?

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

Is One 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.