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How to serve a local decision model that routes support tickets with Kev

Run Kev-4B locally and send a ticket with department, escalate and frustration questions in one request.

Short answer: Run git clone https://github.com/jaredpalmer/kev.git && cd kev; then install the server extras with uv sync --extra serve; start the server: uv run --extra serve python -m kev.serve --run jaredpalmer/kev-4b --port 8009. All steps.

Who it is forA support engineer who wants routing and escalation decisions without per-call API costs.

The problemChat models give free text that needs parsing and their confidence is not calibrated.

The outcomeStructured answers with calibrated probabilities, such as returns, a 0.93 escalate probability and a frustration score.

What you need first

  • Python with uv; install from source (pip install kev is a different package)
  • A GPU (CUDA) or an Apple Silicon Mac with MLX; Kev-0.8B runs on a laptop
  • Optional: a Modal account for fine-tuning and hosted endpoints

Steps

  1. Run git clone https://github.com/jaredpalmer/kev.git && cd kev
  2. Install the server extras with uv sync --extra serve
  3. Start the server: uv run --extra serve python -m kev.serve --run jaredpalmer/kev-4b --port 8009
  4. POST a ticket with choice, noul and score questions to localhost:8009/v1/systemone as in the README curl example
  5. Route tickets by the returned probabilities and thresholds you choose

Prompt for your agent

Set up jaredpalmer/kev with Kev-4B on port 8009, then write a script that sends each ticket in <file> with a department choice, an escalate noul and a frustration score, and writes the answers and probabilities to a CSV.

Caveat. Do not pip install Kev: that name on PyPI is an unrelated ORM. Install from source as shown.

Use this in your AI agent

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Using the Deduped MCP, get the setup steps for jaredpalmer/kev and "Serve a local decision model that routes support tickets"

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