From the source
Today, we release d1-3B and d1-omni-600M, two open-weight models in our d1 decision model family . d1-3B scores 48.57 on the Decision Index v0.2.1 (public split), ahead of every model under 10B and on par with Decider 35B-A3B, a decision model 12x its size.
It runs the full NVIDIA stack, from DGX in the data center to Jetson at the edge: d1-3B answers a question in 8 ms on an NVIDIA GeForce RTX 4090, 16 ms on a Jetson AGX Thor, and 26 ms on a Jetson AGX Orin.
Even the Jetson Orin Nano runs it in 50 ms, fast enough for real-time decisions on the smallest edge hardware. d1-omni-600M is our first experimental checkpoint, handling both text and image, as well as text and audio.
It scores 15.95 on the same index. d1-3B and d1-omni-600M models are available today on Hugging Face.
Check out our docs on how to run them locally.
Architecture and Training Unlike our generative Liquid Foundation Models (LFMs), our d1 decision models don’t produce tokens.
Instead, they produce an 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.
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