JADE

Just Another Decision Engine.

Give JADE some context, a question, and a set of options. It returns a decision and a probability for each option. It can also answer yes/no questions with a probability. There is no generated explanation to parse.

JADE is built on Qwen3.8-27B, with rank-8 LoRA adapters and a trained 255-way decision head. This release was trained on 160,017 synthetic examples covering reasoning, language, classification, retrieval, tools, and probabilistic decisions.

Using JADE

The included inference code uses Python 3.11+ and vLLM. You will need an NVIDIA GPU with enough memory for the 27B base model and its inference cache. We tested this release on a B200 with vLLM 0.30.0.

pip install vllm==0.30.0 huggingface_hub
hf download theunnecessarythings/JADE --local-dir ./jade-model
export PYTHONPATH="$PWD/jade-model:$PYTHONPATH"

from jade import JadeEngine

engine = JadeEngine(model="./jade-model")

response, _ = engine(
    state="Move my meeting with Sam to tomorrow at 3 pm.",
    questions={
        "tool": {
            "type": "choice",
            "instructions": "Which tool should handle this request?",
            "criteria": {
                "calendar": "Find and update a calendar event",
                "email": "Search the email inbox",
                "notes": "Create a note",
            },
        }
    },
)

print(response["answers"]["tool"])
# Contains "choice" and a "probabilities" mapping for all three options.

For a yes/no question, use "type": "noul". Its answer contains "noul", the probability of yes. You can supply several named questions in one call.

The engine supports text inputs, up to 255 options per question, and 8,192 tokens including the answer token. Images and score questions are not supported by this release. Inputs that exceed these limits raise an error.

Training

JADE was trained on JADE-Data, a synthetic corpus of 160,017 decision-making examples. Labels are derived from programs, simulators, solvers, and structured world state.

Setting	Value
Backbone	Qwen3.8-27B, BF16
LoRA rank / alpha	8 / 16
Learning rate	5e-5
Effective batch size	128
Training	One epoch, 160,017 examples
Selected checkpoint	Step 1,251

A temperature of 0.288644 was fitted on a separate calibration partition. The inference code applies it to every question.

Evaluation

To run the official suite, follow the Decision Index instructions. After building and verifying the suite, use the included adapter:

python -m decision_index pipeline --edition 0.2.1 \
  --suite-dir /path/to/suite-0.2 \
  --engine jade.index:DecisionIndexEngine \
  --model "$PWD/jade-model" \
  --out runs/jade

License

JADE is an independent model, not Jev, and is not affiliated with TypeSafe AI. The weights and original release code are Apache-2.0. The prompt renderer is adapted from AutoJev; its MIT license is included in LICENSE-AutoJev.