jev-local

Run Kev decision models on an Apple Silicon Mac and call them from Claude through MCP.

Each call gives the model a text and a question. It returns a calibrated answer instead of generated text:

• choose: pick one of several options

• yes_no: answer a yes/no question

• score: place the text on an ordered scale

Install

You need an Apple Silicon Mac and uv. Kev-4B needs about 16 GB of memory while loading.

git clone https://github.com/phykn/jev-local.git && cd jev-local
git clone https://github.com/jaredpalmer/kev.git vendor/kev && git -C vendor/kev checkout kev-1.0
uv sync --python 3.13

Related MCP server: mcp-laya

Use

.venv/bin/kevctl start kev-4b                                     # uses only weights already in weight/
claude mcp add kev --scope user -- "$PWD/.venv/bin/kev-mcp"

Start a new Claude Code session and ask, for example: "Use kev to decide whether this ticket is urgent: ...".

• As a Mac app: run scripts/build-app.sh once, then open JEV-LOCAL.app, pick a model and press Start. Quitting the app stops the server.

• Stop the model: kevctl stop kev-4b

• Check what is running: kevctl status

kevctl does not download weights. To download a missing model, run it once with HF_HUB_OFFLINE=0 .venv/bin/kevctl start <model>.

Every answer includes a confidence value. When low_confidence is true, check the answer before acting on it.

Models

The models are listed in models.toml. kevctl, the MCP server and the evaluation script all read this file.

To add a model, such as your own fine-tuned checkpoint, add an entry:

[models."my-4b"]
backend = "kev-http"
run = "checkpoints/my-4b"   # local checkpoint directory, or a Hugging Face id@revision
port = 8012
memory_gb = 16
min_confidence = 0.5        # answers below this confidence are marked low_confidence

Layout

• src/jev_local/: the Python package. registry.py reads models.toml, backends.py calls a running Kev server, ctl.py is kevctl and mcp_server.py is kev-mcp.

• app/: the Mac app in Swift. scripts/build-app.sh builds it into JEV-LOCAL.app.

• scripts/evaluate.py and evals/: evaluation. tests/: pytest.

Evaluate and test

.venv/bin/python scripts/evaluate.py --data evals/ko/smoke.jsonl   # accuracy, Brier score, coverage at 5% error
.venv/bin/pytest

scripts/evaluate.py runs every model that is currently running.

License

MIT. Kev and its weights are licensed separately under Apache-2.0.