d1

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d1-3B

d1-3B is a 3B parameter decision model built on LFM2.5-VL-3B. You give it a state (text, JSON, images, or a mix) and a set of questions. It returns calibrated, typed answers in one forward pass with zero output tokens.

• Best decision model under 10B on the Decision Index 0.2.1: 48.57, ahead of every 4B and 9B model and of Decider 35B-A3B (47.11).
• Multimodal: images and text in the same state. It scores 74.1 on 11 public image benchmarks (LFM2.5-VL-3B: 73.9).
• Fast: 8 ms a decision on an NVIDIA RTX 4090, 9 ms on an AMD MI325X, 30 ms on an Apple M5 Pro.

Find more information about open d1 in our blog post.

[!NOTE] 💻 Demos: Try d1-3B in a Hugging Face space without any setup: Open d1 Arcade: Collection of 10 demos using d1-3B

🗒️ Model Details

d1-3B is a multimodal decision model with the following features:

• Total parameters: 3.12B
• Vision encoder: SigLIP2 NaFlex shape-optimized 400M
• Context length: 32,768 tokens
• Vocabulary size: 128,000

We recommend d1-3B wherever a pipeline needs a yes/no, a pick from named options, or a rating: routing and triage, moderation, intent and topic classification, extraction checks, reranking, LLM-as-a-judge scoring, agent guardrails, and visual inspection. It is not a chat model and does not write text.

🏃 How to use

Install the dependencies (requires transformers>=5.14):

pip install "transformers>=5.14" torch torchvision pillow

The model ships its own code, so load it with trust_remote_code=True:

import torch
from transformers import AutoModel
from transformers.image_utils import load_image

device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
dtype = torch.float32 if device == "cpu" else torch.bfloat16
model = AutoModel.from_pretrained("LiquidAI/d1-3B", trust_remote_code=True, dtype=dtype).to(device)

# Text: several named questions over one state, answered in one pass
questions = {
    "refund": {
        "type": "noul",
        "instructions": "Is the customer asking for a refund?",
    },
    "team": {
        "type": "choice",
        "instructions": "Which team should handle this?",
        "criteria": {
            "billing": "Charges, refunds, invoices",
            "technical": "App or site faults",
            "fraud": "Suspected unauthorised use",
        },
    },
    "urgency": {
        "type": "score",
        "instructions": "How urgent is this?",
        "criteria": ["Can wait", "Today", "Blocking the customer now"],
    },
}
print(model.system_one("I was charged twice this month, please refund one of them.", questions))

# Image: the photo is the whole state
image = load_image("http://images.cocodataset.org/val2017/000000039769.jpg")  # two cats on a sofa
cats = {
    "type": "choice",
    "instructions": "How many cats are there?",
    "criteria": {"one": "One", "two": "Two", "more": "Three or more"},
}
print(model.system_one(None, {"cats": cats}, images=[image]))

# Batch: many requests, packed together with no padding
tickets = ["Where is my parcel? It was due Monday.", "The app crashes when I open settings."]
print(model.system_one_batch([(t, {"team": questions["team"]}) for t in tickets]))

A state is a string, any JSON value, or None when the images are the whole state.

Questions and answers

Questions follow the Decision Index schema: type, instructions, and criteria.

Each call returns {"answers": {name: answer}, "usage": {"input_tokens": n, "output_tokens": 0}}.

⚡ Speed

Warm calls, one request at a time: a single question, three questions over one state, a 3.4k-token state and a 384 px image. The last column is throughput with 64 states packed into one pass.

Edge Inference

We measure latency on an Apple M5 Pro and, in collaboration with NVIDIA, on an NVIDIA Jetson AGX Thor, a Jetson AGX Orin 64 GB and a Jetson Orin Nano.

GPU Inference

We measure latency on an NVIDIA RTX 4090 and an AMD MI325X, in bf16, median of 20 runs.