Liquid AI Releases Open-Weight d1-3B and d1-omni-600M: Multimodal Decision Models With Zero Output Tokens

來源原標題

Liquid AI Releases Open-Weight d1-3B and d1-omni-600M: Multimodal Decision Models With Zero Output Tokens

作者

Asif Razzaq

原始標籤

Agentic AI · AI Infrastructure · AI Shorts · Applications · Artificial Intelligence · Decision Models · Editors Pick · For Devs · Language Model · Large Language Model · Machine Learning · New Releases · Open Source · Staff · Tech News · Technology

原始連結

https://www.marktechpost.com/2026/10/07/liquid-ai-releases-open-weight-d1-3b-and-d1-omni-600m-multimodal-decision-models-with-zero-output-tokens/

摘要原文

Liquid AI has released Open d1 , two open-weight multimodal models in its d1 decision model family. d1-3B reads text and images. d1-omni-600M reads text with an image, or text with audio. Neither model writes text. Each returns calibrated, typed answers in one forward pass with zero output tokens. The target is real-time decisions on the NVIDIA stack: DGX servers, RTX workstations, and Jetson edge boards. Is it deployable? Yes. Both checkpoints are on Hugging Face, load through Transformers, and have day-one llama.cpp support. The LFM Open License v1.0 allows free commercial use below $10 million in annual revenue. d1-omni-600M is an early research release with no published latency figures. A generative LLM writes its answer token by token, and your code parses it. A decision model takes a state and a set of named questions. It reads them once and returns a probability for every allowed answer. The Liquid AI define three question types: Several questions can share one state in a single call. Every response reports output_tokens: 0 . Liquid AI recommends d1 for routing, moderation, intent classification, reranking, LLM-as-a-judge scoring, agent guardrails, and visual inspection. Neither checkpoint is a chat model. d1-3B has 3.12B parameters and starts from LFM2.5-VL-3B , a decoder-only vision-language model. Liquid AI averaged the weights of LFM2.5-2.6B with that model’s text backbone. It then fine-tuned several checkpoints with different seeds and data mixtures, and merged them again. It uses a 400M SigLIP2 NaFlex vision encoder and a 32,768-token context. Long inputs, shuffled answer options, and fixing data shortcuts mattered more than advanced techniques. d1-omni-600M has 587M parameters and starts from LFM2.5-Encoder-350M , a bidirectional encoder. That covers a 381M shared trunk and decision head, a 94M vision encoder, and a 112M audio encoder. The audio encoder is a 17-layer FastConformer. Context is 16,384 tokens. Audio clips are capped at 30 seconds. A request carries images or audio, never both. Audio training covered English speaker-to-assistant requests only. On Decision Index v0.2.1 , d1-3B scores 48.57. That beats every model under 10B and edges Decider 35B-A3B (47.11). Only Winnow-12B scores higher at 50.02. Liquid AI ran the official scorer itself, so d1 scores are not leaderboard submissions. d1-3B leads the Tools (74.5) and Arts (36.3) categories but trails on Knowledge (23.8). Across seven public text benchmarks, d1-3B averages 82.9, ahead of Decider 4B at 81.1. d1-omni-600M averages 78.4 and posts the top Civil Comments (95.8) and PAWS-X (79.5) scores. On 11 image benchmarks, d1-3B averages 74.1 against 73.9 for its base model. Liquid AI calls audio decision benchmarks an open problem. Liquid AI measured end-to-end latency, one warm request at a time. One question takes 8 ms on an RTX 4090 and 9 ms on an AMD MI325X. On Jetson, AGX Thor takes 16 ms, AGX Orin takes 26 ms, and Orin Nano takes 50 ms. On Jetson AGX Thor, three questions over one state take 20 ms versus 16 ms for one. The RTX 4090 figure uses model.compile(mode="reduce-overhead") . Without it, one question takes 16 ms. NVIDIA’s Jetson AI Lab also hosts d1-3B guides. Sources: d1-3B , d1-omni-600M , Decider 4B , Decider 35B-A3B , Winnow-12B model cards. Index scores as listed on the d1 cards. Latencies use different hardware and workloads, so they are not directly comparable. d1-3B needs transformers>=5.14 and trust_remote_code=True . d1-omni-600M needs transformers>=5.15 . Both expose system_one(state, questions) for one state and system_one_batch for packed requests. Liquid AI recommends float16 for d1-omni-600M on GPU, since bfloat16 changed some top answers. You can try ten camera-driven d1-3B demos in the Open d1 Arcade space. With NVIDIA, Liquid AI also showed d1-3B navigating Isaac Sim from a Jetson. Check out the d1-3B , d1-omni-600M and Technical details . All credit goes to the researcher of this project. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter . Wait! are you on telegram? now you can join us on telegram as well. [Sponsored] The web is the one API most agents are missing. Databases, calendars and repos have APIs. The open web mostly doesn’t. The TinyFish MCP server gives any MCP client four tools: TinySearch, TinyFetch (full pages as markdown, JavaScript included), TinyBrowser for logins and forms, and TinyAgent for multi-step jobs. Search and Fetch are free. The post Liquid AI Releases Open-Weight d1-3B and d1-omni-600M: Multimodal Decision Models With Zero Output Tokens appeared first on MarkTechPost .