Futurix-AI/FxAI-decision-model

FxAI Decision Model

FxAI Decision Model is a trained decision classifier on the Qwen/Qwen3.8-27B architecture. Given a state, a question and up to 255 candidate options, it returns a calibrated probability distribution over those options.

The repository contains the complete 26.1B checkpoint and the Futurix Decision SDK.

The model completed the Decision Index 0.3 public suite with a public score of 62.26.

Model details

• Task: typed decision classification and ranking
• Architecture: Qwen3.8-27B multimodal backbone
• Parameters: 26.1B
• Context limit: 8,192 tokens through the bundled decision interface
• Options: 2–255 per question through the Decision SDK
• Attention: noncausal full attention in full-attention layers
• Pooling: last token
• Output: calibrated option probabilities
• Languages evaluated: primarily English

Intended use

This model is intended for routing, intent classification, multiple-choice decisions, tool selection and structured scoring.

It is not intended for autonomous high-stakes decisions, safety-critical control, medical or legal advice, or use without application-specific evaluation and human review.

Inference

This repository includes the Futurix Decision SDK under futurix_decision/. Use that runtime for decision probabilities. transformers.AutoModel alone does not return them. The Hugging Face Inference widget is disabled.

Requirements

• Linux, Python 3.12 and an NVIDIA CUDA GPU with bfloat16 support.
• The checkpoint is about 29 GB. A 48 GB GPU is recommended for inputs up to 8,192 tokens.
• CPU inference is supported but is not a practical way to run this 27B model.

Clone the repository with Git LFS, create an isolated environment and install the runtime used for verification:

git lfs install
git clone https://huggingface.co/Futurix-AI/FxAI-decision-model
cd FxAI-decision-model
python3 -m venv .venv
source .venv/bin/activate
python3 -m pip install --upgrade pip
python3 -m pip install -r requirements.txt
python3 example.py --device cuda:0

To try another input:

python3 example.py \
  --state "I was charged twice and need a refund." \
  --option billing="Charges, invoices and refunds" \
  --option support="Technical integration errors" \
  --option sales="Questions about purchasing"

Python API

from pathlib import Path

from futurix_decision import DecisionInput, load_model

checkpoint = Path("/path/to/FxAI-decision-model")
model = load_model(
    checkpoint, family="fxai", device="cuda:0",
    max_image_pixels=512 * 512,
)
result = model.decide(DecisionInput(
    state="My integration fails",
    question="Which team?",
    options=["billing", "support"],
    candidate_descriptions=["Charges and refunds", "Integration errors"],
))
print(result.to_dict())

The SDK supports typed choice, noul, and score decisions with 2–255 options. The FxAI runtime supports image inputs, enforces an 8,192-token limit without silent truncation, and returns calibrated option probabilities.

Convenience methods use the same runtime:

choice = model.choice(
    state="The customer was charged twice.",
    question="Which team should handle this?",
    choices=["billing", "support"],
)
binary = model.noul(state="The invoice is paid.", question="Was it paid?")
score = model.score(
    state="The response is mostly correct.",
    question="Rate its quality.",
    min_score=0,
    max_score=5,
)

Training and benchmark checks were text-only. Image accuracy and safety have not been independently evaluated.

Training details

The training strategy is inspired by Bosun. The model is a trained decision classifier on the Qwen3.8-27B architecture.

Decision Index 0.3 public benchmark

The official scorer covered 140,178 scoreable requests over 43 reported benchmarks: 140,148 answered, 30 unsupported, zero errors and zero pending. The public index counts 37 benchmarks.

Result	FxAI-decision-model
Public Decision Index	62.26
Knowledge & Reasoning	51.50%
Language Understanding	67.43%
Retrieval & Classification	61.20%
Tools & Automation	79.68%
Arts & Human Taste	47.01%

Published comparison

The four published decision models nearest this public index, from the Decision Index board snapshot of 20 October 2026, 14:13 UTC (Decision Index board). Area columns are the same chance-corrected public scores, on a 0–100 scale.

Model	Public index	Knowledge	Language	Retrieval	Tools	Arts
FxAI Decision Model	62.26	51.50	67.43	61.20	79.68	47.01
Darwin-27B-ZTC	62.63	44.72	68.46	60.90	86.69	53.34
Sieve 31B	62.52	49.91	65.59	69.55	78.72	43.50
Wald-26B-A4B	62.30	44.71	64.25	69.05	83.28	50.86
OneJev1.6-4B	61.95	45.00	64.40	68.92	81.50	49.75

Candidate-only machine-readable results are available in BENCHMARKS.json.

Evaluation ran in bounded sequential and throughput-batched chunks on one RTX PRO 6000. The mixed full-run timing fields are throughput measurements, not a valid single-request leaderboard latency claim. On the separate 9,192-request sequential component run, median / mean / p80 were 107.5 / 202.5 / 255.5 ms. The maintainers must independently check the private latency gate.

Additional independent holdout

On a separate holdout, expected correctness was 52.56% and soft-label cross-entropy was 1.1454 (lower is better). This result is not part of Decision Index.

Limitations and release status

The 27B checkpoint and GPU requirement are costly. The published figure is the public Decision Index score. The private portion of the Full score and the independent latency gate are available only to the maintainers.