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SelfJev

SelfJev turns text and questions into decisions your code can use. Route a request, check an AI response, or apply a policy. Get typed answers and probabilities back from selfjev-4b, an open 4B decisions model with Jev's API.

SelfJev is self-hosted. There is no SelfJev cloud: you run the model server on your own GPU, and this package is the client for it (plus the server itself, as an extra). Already on Jev? Keep TypeSafe's SDK and change two environment variables.

You need API type What comes back
Does this need a refund? Noul Probability of yes
Which team should handle it? Choice One choice and probabilities for every option
How urgent is it? Score A position on your ordered scale
Which topics are mentioned? Multi Every selected option and its probability

1. Run your server

On a Linux machine with an NVIDIA GPU (24 GB is a practical start; see the hardware guide):

pip install "selfjev[serve,gpu]"
export SELFJEV_API_KEYS="$(python -c 'import secrets; print(secrets.token_urlsafe(32))')"   # your key, you choose it
selfjev serve --host 0.0.0.0 --port 8000

The first start downloads the selfjev-4b adapter (230 MB) and its Qwen3.5-4B base from Hugging Face. --adapter <dir or repo> serves your own fine-tune. Docker, AWS, Runpod and GCP recipes are in the deployment guide.

Extra For
selfjev the client only: httpx + pydantic, no torch
selfjev[serve] the model server (add gpu on Linux for the fast kernels)
selfjev[train] selfjev finetune and selfjev rlcd
selfjev[deploy] selfjev deploy aws

2a. Already using Jev? Change two variables

Code written for TypeSafe's typesafe-sdk runs unchanged against your server:

export TYPESAFE_BASE_URL="https://your-selfjev-host:8000"
export TYPESAFE_API_KEY="the key you set in SELFJEV_API_KEYS"
from typesafe_sdk import Choice, Noul, TypeSafeClient

client = TypeSafeClient()   # reads the two variables above
res = client.system_one(
    state="I was charged twice. Please refund the duplicate payment.",
    questions={
        "refund": Noul(instructions="Does the customer want a refund?"),
        "team": Choice(instructions="Which team?", criteria={"billing": "payments and refunds", "support": "technical issues"}),
    },
)

The default model name jev-latest is answered by selfjev-4b, and client.models.list(), errors and retries behave as they do against Jev. OpenRouter's decisions path (/api/alpha/decisions) is served too.

2b. Or use the selfjev client

pip install selfjev in your application. It adds Multi (select all that apply) and the fine-tuning API.

from selfjev import Choice, Multi, Noul, SelfJev

client = SelfJev(base_url="https://your-selfjev-host:8000", api_key="the key you set in SELFJEV_API_KEYS")

result = client.system_one(
    state="I was charged twice. Please refund the duplicate payment.",
    questions={
        "refund": Noul("Does the customer want a refund?"),
        "team": Choice("Which team should handle this?", {"billing": "payments and refunds", "support": "technical issues"}),
        "topics": Multi(
            "Which topics are mentioned?",
            {"payment": "a payment or charge", "refund": "a refund request", "login": "an account access problem"},
        ),
    },
)

print(result.nouls["refund"].noul)   # probability of yes
print(result.choices["team"].choice)  # selected team
print(result.multis["topics"].multi)  # selected topics

AsyncSelfJev has the same interface with await. SELFJEV_BASE_URL and SELFJEV_API_KEY work in place of the arguments.

Measured

SelfJev-4B served by TreeServer, compared with Jev on the same questions:

Suite Questions SelfJev-4B Jev
Text decisions 1,991 95.7% 97.2%
AI response review 946 93.1% 92.5%
Broader text tasks (development benchmark) 3,471 83.8% 82.7%

These are project evaluations, not a universal ranking. See the results and limitations and the Decision Bench evaluation set.

The package code is Apache-2.0. The model weights carry their own licenses; see each model card.

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