Multimodal open d1 decision models for the edge

This post introduces the d1 decision model family (d1-3B and d1-omni-600M) for edge deployment. d1-3B is built on LFM2.5-VL-3B (decoder-only, text+image input) and d1-omni-600M is built on LFM2.5-Encoder-350M (bidirectional encoder, text+image or text+audio input). Both models are designed for single forward pass decision making, not token generation. Benchmarks show d1-3B achieving high scores on various decision tasks and outperforming larger models. Performance metrics on NVIDIA edge devices (Jetson AGX Thor, Orin, Nano) and GPUs (RTX 4090, AMD MI325X) demonstrate low latency for single and multiple questions, as well as image processing. The post provides Python code examples for loading and using the models, including batching capabilities.