Matilda Decision v1

Matilda Decision is a 27B multimodal decision model from Maincode. You give it an input (text or JSON, optionally with images) and the questions you need answered: pick one of several options, say yes or no, or rate on a scale. It answers them all in one call and returns a probability for every answer, without generating text.

Evaluation

These are results on the public sets only, for now.

Text: Jev Decision Index 0.3, public suite

All 140,178 public requests answered, none unsupported.

_{Matilda Decision: our run with the official 0.3 kit. Others: Decision Index 0.3 board, public results (area scores computed from the board's per-benchmark results with the kit's own scoring).}

Quickstart

pip install "transformers==5.17.0" torch torchvision pillow safetensors
pip install flash-linear-attention  # optional: fast GPU kernels for the linear-attention layers

import json

from transformers import AutoModel

model = AutoModel.from_pretrained("Maincode/matilda-decision-v1", trust_remote_code=True)

# Text
result = model.system_one(
    state="The order arrived damaged yesterday. The customer has a receipt and asks for a replacement today.",
    questions={
        "route": {
            "type": "choice",
            "instructions": "Which team should handle this request?",
            "criteria": {
                "returns": "Refunds, replacements and damaged deliveries",
                "billing": "Payments, invoices and charges",
                "technical": "Product setup and faults",
            },
        },
        "receipt": {"type": "noul", "instructions": "Does the customer have a receipt?"},
        "urgency": {
            "type": "score",
            "instructions": "How urgent is this request?",
            "criteria": ["Routine", "Soon", "Today"],
        },
    },
)
print(json.dumps(result["answers"], indent=2))

# Text and images
result = model.system_one(
    state="The customer says the blender arrived cracked and attached the receipt.",
    images=["receipt.png"],  # local paths, http(s) URLs, PIL images or base64 data URLs
    questions={
        "on_receipt": {"type": "noul", "instructions": "Does the receipt list the blender?"},
        "payment": {
            "type": "choice",
            "instructions": "How was the order paid?",
            "criteria": {"card": None, "cash": None, "gift card": None},
        },
    },
)
print(json.dumps(result["answers"], indent=2))

from_pretrained takes device (default cuda:0 when a GPU is visible) and max_image_pixels (see Images).

Requests and answers

system_one(state=..., questions={id: question, ...}, images=[...]) returns {"model", "answers": {id: answer}, "usage"}. Every question sees the same state and images.

instructions is the question text. state can be a string or any JSON value. It is read as data, not as instructions. Inputs are never truncated: a request over 131,072 tokens raises an error.

Images

• Size: each image is resized, keeping its aspect ratio, to at most 1,638,400 pixels (1.6 MP, about 1280 × 1280) and at least 65,536. All the results above were measured at this setting, which is the default in the runtime and in processor_config.json.
• Tokens: one input token per 32 × 32 pixel patch, so about 1,600 for a 1.6 MP image and about 260 for 512 × 512.
• Order: images go before the text, in the order given.
• Lower cap: for speed or memory, use AutoModel.from_pretrained(..., max_image_pixels=...). Expect lower accuracy on fine detail such as charts, documents and small text. Caps above 1.6 MP are not validated.

Architecture

Qwen3.8-27B vision-language backbone with a 255-option decision readout. Each question takes one forward pass, and its answer is read from the final prompt position.

Training: post-trained from Qwen3.8-27B in several stages: supervised fine-tuning on decisions across text and images, then reinforcement learning.

The model was trained with bidirectional attention: its full-attention layers see the whole prompt at once, rather than only the tokens before each position. It must be served the same way. trust_remote_code=True loads the bundled runtime (matilda_decision_runtime.py), which sets this up. Loading the weights as a plain causal backbone, or serving them in a causal engine, gives different and worse answers.

License

Apache-2.0 (LICENSE). Built on Qwen/Qwen3.8-27B (Apache-2.0).