jiwo

jiwo models read a state and typed questions, and return a probability for every option in one forward pass. They do not generate text. On one H100, the median request takes about 50 ms.

The request and response format is the format of TypeSafe's Jev API (POST /v1/systemone). jiwo is an independent project. TypeSafe does not endorse it.

Both models are on the official Decision Index board. Its Full score combines the public benchmarks (20%) with private tests that the board maintainers run (80%).

The board ranks come from the board of 2026-10-07. Both v2 models (jiwo-4b 2026-10-09, jiwo-0.8b 2026-10-10) use the same recipe as v1 with a larger and more varied training mix. On the public part of 0.3, jiwo-0.8b v2 scores 29.58 (v1: 28.72) and jiwo-4b v2 scores 46.06 (v1: 45.76). On three public datasets that no version trained on, jiwo-0.8b goes from 54.6% to 61.3% and jiwo-4b from 66.3% to 67.8%. Load v1 with revision="v1", for example DecisionModel.from_pretrained("eljiwo/jiwo-0.8b", revision="v1").

Against the untrained base model on Decision Index 0.2.1, with the same server and calibration: jiwo-0.8b v2 scores 28.72 (Qwen3.5-0.8B: 6.99) and jiwo-4b v2 scores 45.08 (Qwen3.5-4B: 29.20).

On a GPU, serve with JIWO_CUDA_GRAPHS=1 (see Settings). On one RTX PRO 6000, the median request takes 13.1 ms for jiwo-0.8b (26.6 ms without graphs) and 31.6 ms for jiwo-4b (34.5 ms).

The training data is mostly English. Other languages are not evaluated.

jiwo needs Python 3.12.

pip install "jiwo @ git+https://github.com/jiwidi/jiwo"

Or work in a checkout with uv:

git clone https://github.com/jiwidi/jiwo
cd jiwo
uv sync                  # on NVIDIA GPUs, add --extra cuda for the fast linear-attention kernels

On a CPU, use float32 (the default). Torch has no fast bfloat16 matrix product on many CPUs. On Apple silicon, bfloat16 is several times slower than float32.

from jiwo.model import DecisionModel

model = DecisionModel.from_pretrained("eljiwo/jiwo-0.8b")
question = {"type": "noul", "instructions": "The customer is angry."}
response = model.decide("Refund please, the parcel arrived crushed.", {"angry": question})
print(response["answers"]["angry"]["noul"])  # the probability that the statement is true

from_pretrained also accepts a local checkpoint directory. Use eljiwo/jiwo-4b for the larger model. The first call downloads the model into the Hugging Face cache.

JIWO_CHECKPOINT=eljiwo/jiwo-0.8b jiwo serve

The server listens on 127.0.0.1:8765. It answers POST /v1/systemone. GET /health and GET /v1/models give the status and the model name.

Send one request:

curl -s localhost:8765/v1/systemone -H 'content-type: application/json' -d '{
  "state": "Refund please, the parcel arrived crushed.",
  "questions": {
    "team":  {"type": "choice", "instructions": "Which team handles this?",
              "criteria": {"billing": "Payments and refunds", "shipping": "Damaged or lost parcels", "account": null}},
    "angry": {"type": "noul", "instructions": "The customer is angry."},
    "urgency": {"type": "score", "instructions": "How urgent is it?", "criteria": ["Not urgent", "Soon", "Now"]}}}'

The response has one answer for each question. The numbers below are synthetic. They show the format only.

{
  "model": "jiwo-0.8b",
  "answers": {
    "team": {"type": "choice", "choice": "shipping", "confidence": 0.895,
             "probabilities": {"billing": 0.05, "shipping": 0.93, "account": 0.02}},
    "angry": {"type": "noul", "noul": 0.71},
    "urgency": {"type": "score", "score": 1.1, "confidence": 0.55,
                "legend": {"0": "Not urgent", "1": "Soon", "2": "Now"},
                "probabilities": {"0": 0.1, "1": 0.7, "2": 0.2}}
  },
  "usage": {"input_tokens": 312, "output_tokens": 0}
}

• choice: choice is the most probable option. confidence is (p_best - 1/n) / (1 - 1/n).
• score: score is the expected level, from 0 to n - 1. confidence is 1 minus the expected distance to the most probable level, divided by the mean distance of the levels to the middle of the scale.
• noul: noul is the probability that the statement is true.
• usage.input_tokens is the number of prompt tokens. The model does not generate tokens.

To answer one request without a server, put the request in a file and run jiwo decide eljiwo/jiwo-0.8b request.json. The command prints the response.

A request has a state and named questions. The state is a string, a JSON object or a JSON array. A request can have at most 64 questions. Each question has one of three types:

instructions is optional. It is the question text, or the statement of a noul question.

The server gives these errors. The body is {"error": {"message": "...", "type": "..."}}.

The server reads its settings from the environment.

The server answers one request at a time. After a GPU out-of-memory error, it tries again with smaller batches. A CPU gives no out-of-memory error before the system swaps. On a CPU, keep JIWO_BATCH_TOKENS small.

A checkpoint that holds a LoRA adapter needs the lora extra: pip install "jiwo[lora] @ git+https://github.com/jiwidi/jiwo".

jiwo has an engine for the Decision Index kit. The engine runs the model in the kit's process. It uses the request code of the server, so it gives the same answers as the kit's http engine against jiwo serve with the same settings.

The kit is not on PyPI. Install it from its repository, then install jiwo in the same environment:

git clone https://github.com/apolinario/decision-index
cd decision-index
git checkout 87d4650b42b377c0291a89c1f1a879f9b31082bf    # edition 0.2.1
pip install -e ".[rebuild]"
pip install "jiwo @ git+https://github.com/jiwidi/jiwo"
python -m decision_index pipeline --engine jiwo.index_engine:JiwoEngine --model eljiwo/jiwo-0.8b \
    --option max_length=65536 --out runs/jiwo-0.8b

The kit's README tells how to get the suite. Engine options use --option key=value: revision, device, max_length (default 8192), batch_tokens (default 262144 on CUDA, 16384 on CPU and MPS), cudnn_attention (default true) and model_name (the model name in responses). A prompt that is too long and a choice with too many options become unsupported, as in the http engine. About 340 suite requests have a prompt longer than 8192 tokens, most of them in ToolRet. For a complete run, add --option max_length=65536.

uv sync
uv run hf download trl-internal-testing/tiny-Qwen3_5ForConditionalGeneration    # a tiny random test model
HF_HUB_OFFLINE=1 uv run pytest -q
uv run ruff check . && uv run ruff format --check . && uv run mypy

The code is under the MIT license (see LICENSE). Each model has the license of its base model. Both models above are Apache-2.0.