Multimodal open d1 decision models for the edge

Multimodal open d1 decision models for the edge

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Published October 7, 2026

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Aurelien Lac

Aurelien-Lac

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LiquidAI

Fernando Fernandes Neto

fernandofernandes

Edoardo Mosca

EdoardoMosca

Maxime Labonne

mlabonne

Leonie Monigatti

iamleonie

Today, we release two open decision models in our d1 decision model family : d1-3B and d1-omni-600M (experimental).

• Best decision model under 10B on the Decision Index 0.2.1: d1-3B scores 48.57, ahead of every 4B and 9B model and of Decider 35B-A3B (47.11).

• Multimodal: d1-3B supports text and images, while d1-omni-600M supports text and images or text and audio

• Fast: d1-3B answers a question in 16 ms on an NVIDIA Jetson AGX Thor, 26 ms on a Jetson AGX Orin, and 50ms on a Jetson Orin Nano

How we built decision models for the edge

These open d1 decision models are built on our Liquid Foundation Models (LFMs). Unlike our generative models, decision models don’t produce tokens but answer in a single forward pass.

d1-3B and d1-omni-600M are trained from two very different backbones:

• d1-3B is trained from LFM2.5-VL-3B , our latest VLM, which is decoder-only. It accepts text and images as inputs.

• d1-omni-600M is trained from LFM2.5-Encoder-350M , a bidirectional encoder. It adds vision and audio encoders to handle all three modalities. It accepts either text and image, or text and audio as inputs. This model is currently in an early research release and is undergoing further development.

Benchmark results

We benchmarked d1-3B and d1-omni-600M on seven public datasets spanning reading comprehension, toxicity detection, intent classification, medical QA, and cross-lingual understanding. d1-3B achieves a mean score of 82.9, the highest in the table and above Decider 4B. d1-omni-600M scores 78.4, surpassing Decider 2B (77.1) with only a quarter of the parameters.

Benchmark

d1-omni-600M

d1-3B

Decider 2B

Decider 4B

SQuAD 2.0

74.0

83.3

67.7

76.0

Civil Comments

95.8

93.3

93.6

92.8

MASSIVE intent

86.1

86.9

81.1

88.3

PubMedQA

61.3

68.3

65.7

63.3

BoolQ

77.7

86.3

87.3

89.0

XNLI

74.7

85.6

85.0

88.6

PAWS-X

79.5

76.4

59.5

69.8

Mean

78.4

82.9

77.1

We validated that d1-3B retains the vision capabilities of its LFM2.5-VL-3B backbone on standard vision benchmarks, and that d1-omni-600M handles all three modalities. We do not report any vision or audio benchmarks, as the Decision Index v0.3 includes only a private vision split and audio decision benchmarks are currently an open problem.

Speed

In collaboration with NVIDIA, we evaluated d1-3B on the NVIDIA stack across NVIDIA GeForce RTX 4090, NVIDIA Jetson AGX Thor, Jetson AGX Orin 64 GB, and Jetson Orin Nano. Since d1-omni-600M is an early research release, we don’t report any speed numbers for it in this release.

Edge inference. d1-3B answers a single question in under 50 ms on every measured device. Three questions take only 1.3x the time of one, with the AGX Thor going from 16 ms to 20 ms.

One question

3 questions

3.4K-token state

384px image

64 states, packed

Apple M5 Pro

30 ms

41 ms

640 ms

62 ms

78 / s

Jetson AGX Thor

16 ms

20 ms

220 ms

35 ms

262 / s

Jetson AGX Orin 64 GB

26 ms

560 ms

83 ms

110 / s

Jetson Orin Nano

50 ms

73 ms

1,640 ms

202 ms

38 / s

GPU inference. On GPU, d1-3B answers a question in under 10 ms and processes a 384px image in under 18 ms on both platforms.

NVIDIA RTX 4090

8 ms

21 ms

102 ms

17 ms

475 / s

AMD MI325X

9 ms

14 ms

44 ms

18 ms

1,106 / s

How to use open d1 decision models

Reach for d1 decision models when you need fast, structured decisions, including multimodal inputs. d1-3B delivers the highest decision quality at its size, while d1-omni-600M fits where footprint matters.

Install the dependencies (requires transformers>=5.14 ):

pip install
"transformers>=5.14"
torch torchvision pillow

These model ship their own code, so load it with trust_remote_code=True :

import
io
import
urllib.request
import
torch
from
PIL
import
Image
from
transformers
import
AutoModel

device =
"cuda"
if
torch.cuda.is_available()
else
"mps"
if
torch.backends.mps.is_available()
else
"cpu"
model = AutoModel.from_pretrained(
"LiquidAI/d1-3B"
, trust_remote_code=
True
,
                                  dtype=torch.float32
if
device ==
"cpu"
else
torch.bfloat16).to(device)
# Several named questions over one text 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))
# An image as the whole state
url =
"http://images.cocodataset.org/val2017/000000039769.jpg"
# two cats on a sofa
photo = Image.
open
(io.BytesIO(urllib.request.urlopen(url).read()))
print
(model.system_one(
None
, {
"cats"
: {
"type"
:
"choice"
,
"instructions"
:
"How many cats are there?"
,
"criteria"
: {
"one"
:
"One"
,
"two"
:
"Two"
,
"more"
:
"Three or more"
}}},
                       images=[photo]))
# 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]))

For brevity, we only include the example for d1-3B. See the d1-omni-600M model card for instructions on how to run it.

Get Started with open d1 decision models

Both decision models are open-weight and available on Hugging Face today:

• Download: d1-3B and d1-omni-600M on Hugging Face.

• Try: run the demos in our System One Arcade Hugging Face Space.

We can't wait to see what you build.

Citation

If you use this work, please cite the release blog:

@article{liquidAI2026opend1,
  author  = {Liquid AI},
  title   = {Open d1: Edge decision models for text, vision, and audio},
  journal = {Liquid AI Blog},
  year    = {2026},
  note    = {www.liquid.ai/blog/open-d1},
}

Models mentioned in this article 3

Spaces mentioned in this article 1

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