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How to serve a decision model behind a local API with RSI Jev

Run v6.1-VL 4B locally and get calibrated yes/no or pick-one answers over HTTP.

Who it is forA developer whose app needs a fast yes/no or pick-one answer on text or images, with no per-call generation cost.

The problemCalling a general chat model for every routing or classification decision is slow and spends tokens on reasoning the task does not need.

The outcomeA local server that returns a probability for each option, plus the layer that answered and its confidence.

What you need first

  • Python with pip and git installed
  • A GPU with enough memory for the chosen model, or an Apple silicon Mac for the MLX builds
  • A 96 GB GPU if you want to serve the 27B model

Steps

  1. Install the package with pip install "rsi-jev[fast,vision] @ git+https://github.com/Shanghua-Gao/RSI-Jev"
  2. Start the server with rsi-jev serve v6.1-vl-4b --effort auto --port 8000
  3. Send a test question with curl localhost:8000/v1/systemone -H 'Content-Type: application/json' -d '{"model": "jev-latest", "state": "I was charged twice. Please refund.", "questions": {"refund": {"type": "noul", "instructions": "Does the user request a refund?"}}}' (inferred, check the README)
  4. Check that the response reports the layer that answered and its confidence

Prompt for your agent

In the Shanghua-Gao/RSI-Jev repo, install the package with pip, then run `rsi-jev serve v6.1-vl-4b --effort auto --port 8000`. Send the <question> about the text <input> to localhost:8000/v1/systemone and return the probability for each option. Verify that the response includes the layer that answered and a confidence value.

Caveat. The public Decision Index scores are for full runs of the default configuration, so your own results on different questions will vary.

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