--- tags: - decision-model - classification - text-classification license: apache-2.0 language: - en library_name: transformers pipeline_tag: text-classification inference: false --- # FxAI Decision Model ![FxAI Decision Model](assets/fxai-decision-model-banner.png) FxAI Decision Model is a trained decision classifier on the [Qwen/Qwen3.8-27B](https://huggingface.co/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: ```bash 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: ```bash 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 ```python 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: ```python 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](https://huggingface.co/spaces/multimodalart/jev-decision-index)). 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.