selfjev/GitHub
GET STARTED / SELFJEV

Quickstart

Your first self-hosted decision.

SelfJev turns a state (your text) and a set of questions into typed answers with probabilities. You run the model on your own GPU; your application calls it over HTTP.

Get the model

The releases are public on Hugging Face:

  • SelfJev-4B adapter: the trained LoRA adapter for the native tree engine. The quickstart below uses this adapter from the repository.
  • SelfJev-4B full merged model: the complete weights, tokenizer and configuration. Its model card includes download and vLLM serving instructions.
  • SelfJev Decision Bench: the evaluation questions, expected answers and scoring tools.

1. Start a GPU server

Use a Linux machine with an NVIDIA GPU. Start with 24 GB VRAM, 4 vCPUs, 16–32 GB system RAM, and 50 GB free disk. These are planning recommendations, not a tested minimum. See hardware and sizing for the evidence and limits.

Install Git, Git LFS, and uv. Verify that nvidia-smi sees your GPU. Then:

bash
git clone https://github.com/Jwuthri/SelfJev.git
cd SelfJev
git lfs install
git lfs pull --include "weights/selfjev_4b/*"
uv sync --frozen --no-dev --extra serve --extra gpu
export SELFJEV_API_KEYS="replace-with-a-long-random-key"
uv run --no-sync selfjev serve --host 0.0.0.0 --port 8000

You choose this key; no external provider issues it. Generate a long random value, for example with python -c 'import secrets; print(secrets.token_urlsafe(32))', and keep it private. Copy the same value into the client’s api_key below. The server checks that the two match. If you leave SELFJEV_API_KEYS unset, the server accepts requests without a key, which is suitable only on a trusted local network.

The pinned base model downloads on first start (about 9 GB). Keep the terminal running. Model loading can take several minutes. Check readiness in another terminal:

bash
curl --fail http://localhost:8000/health

Prefer a private network or SSH tunnel during setup. Configure HTTPS before sending credentials or private text over a public network. Docker, AWS, Runpod, and Google Cloud have separate deployment guides.

2. Install the lightweight client

On the machine running your application, no GPU or model download is needed:

bash
pip install "selfjev @ git+https://github.com/Jwuthri/SelfJev"

3. Ask your first questions

With the server on the same machine, or an SSH tunnel forwarding local port 8000:

python
from selfjev import SelfJev, Noul, Choice

client = SelfJev(
    base_url="http://localhost:8000",
    api_key="replace-with-the-same-key-set-on-your-server",
)
result = client.system_one(
    state="My invoice was charged twice. Please refund the second charge.",
    questions={
        "refund": Noul("Does the customer ask for a refund?"),
        "team": Choice("Which team should handle this?", {
            "billing": "payments and refunds",
            "tech": "outages and bugs",
        }),
    },
)
print(result.nouls["refund"].noul)
print(result.choices["team"].choice)

noul is a probability, and choice is one of your option keys. Results depend on the model; the examples on the home page are illustrations, not live inference.

The API reference covers all four answer types. How the model works explains shared context and isolated branches. For the evaluation history, use the research explorer or the full lab notebook.

View source Hardware & sizing