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How to locate a target object in an image with boxes with LLM2 Jev

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

Who it is forA developer building an image review or screen-analysis tool who needs object locations from a model.

The problemGetting coordinates from a model usually means parsing free text, which is fragile.

The outcomeNormalized bounding boxes returned as typed objects, which can be combined with Choice or Score 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. Follow the Box guide in docs/en/multimodal.md to send an image with a Box question for the target (inferred, check the README)

Prompt for your agent

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.

Caveat. The README says Box generates normalized coordinates, but it does not give the request format, so read the Box guide before writing code.

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