INTRODUCING

Decision 3.0

d3-flash 9B

Towards Open Multimodal Foundation Decision Models

ONE CALL · MANY DECISIONS

Every input.
One call.

Give d3-flash text or JSON with images and videos, and ask the questions you need answered. It answers them all in one call and returns a probability for every option, without generating text.

Choice: Pick an option
Yes / No: A probability of yes
Score: A level on your scale

Text or JSON Any context, shared by every question. Nothing is truncated.
Images Several per request: PNG, JPEG or WebP, each read at up to 1.6 MP.
Video MP4, WebM, MOV or MKV, read at 2 frames per second, up to 32 frames.

THE STUDIO

Your input. Your decisions.

Pick an example, or drop your own image or video, and ask Choice, Yes / No and Score questions about it.

Text, images and video in one request.

Model

d3-flash9B v3.1.0Checking

Auto-run

Try an example

01Input

Example

Context

Every question sees the context and all media

Images are read at up to 1.6 MP; videos at 2 frames per second, up to 32 frames. Nothing is truncated.

02Decisions

—

Ready when you are

Meet d3-flash.

d3-flash is the 9B multimodal foundation decision model of Decision 3.0. Give it text or JSON with images and videos and the questions you need answered; it returns a probability for every option.

Parameters: 8.39B, including the 0.46B vision encoder
Inputs: Text or JSON, images and videos (several per request)
Decision types: Choice · Yes / No · Score
Release: v3.1.0
Serving here: AMD Instinct MI300X
License: Apache-2.0

Quickstart

from transformers import AutoModel

model = AutoModel.from_pretrained(
    "vllm-sr/d3-flash", revision="v3.1.0", trust_remote_code=True
)
result = model.system_one(
    state="The blender arrived cracked; the receipt is attached.",
    images=["receipt.png"],    # paths, URLs, PIL images or data URLs
    videos=["unboxing.mp4"],   # read at 2 frames per second
    questions={
        "route": {
            "type": "choice",
            "instructions": "Which team should handle this?",
            "criteria": {"returns": None, "billing": None},
        },
        "on_receipt": {
            "type": "noul",
            "instructions": "Does the receipt list the blender?",
        },
        "urgency": {
            "type": "score",
            "instructions": "How urgent is it?",
            "criteria": ["Routine", "Soon", "Today"],
        },
    },
)
print(result["answers"])