Multimodal open d1 decision models for the edge

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	81.1

Speed

How to use open d1 decision models

Install the dependencies (requires transformers>=5.14):