d1-2B
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d1-2B-GGUF
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View details ↗d1-3B
LiquidAId1-3B is a 3B parameter **decision model** built on LFM2.5-VL-3B. You give it a state (text, JSON, images, or a mix) and a set of questions. It returns calibrated, typed answers in **one forward pass with zero output tokens**. - **Best decision model under 10B on the Decision Index 0.2.1**: 48.57, ahead of every 4B and 9B model and of Decider 35B-A3B (47.11). - **Multimodal**: images and text in the same state. It scores 74.1 on 11 public image benchmarks (LFM2.5-VL-3B: 73.9). - **Fast**: 8 ms a decision on an NVIDIA RTX 4090, 9 ms on an AMD MI325X, 30 ms on an Apple M5 Pro. Find more information about open d1 in our blog post.
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View details ↗d1-3B-GGUF
LiquidAId1-3B is a decision model developed by Liquid AI: it answers named, typed questions over a state (text, JSON or images) in one forward pass, with no generated tokens. Find more details in the original model card: https://huggingface.co/LiquidAI/d1-3B
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View details ↗d1-3B-MLX-4bit
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View details ↗d1-3B-MLX-8bit
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View details ↗d1-3B-MLX-fp16
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View details ↗d1-Omni-2B
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View details ↗d1-Omni-2B-GGUF
LiquidAId1-Omni-2B is a decision model for text, pictures, and audio, built on LFM3-Omni-2B. It returns calibrated answers in one forward pass, without generating tokens. llama.cpp answers `noul`, `choice` and `score` questions over a state (text or JSON), pictures, a video, audio clips, and questions written or spoken. Videos are read as their frames, and video input needs `ffmpeg` in `PATH`. Multiple choice (`"multiple": true`), extraction, estimates and boxes are not supported yet. A question is read in one batch, so `-b` and `-ub` must hold its state and media. A picture takes at most half of `-ub`: 8192 keeps the full budget of 4096 tokens per picture, smaller values lower it.
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View details ↗d1-omni-600M
LiquidAId1-omni-600M is a 600M parameter **decision model** built on LFM2.5-Encoder-350M. You give it a state (text or JSON, with images or a voice clip) and a set of named questions. It returns typed answers with **zero output tokens**: every answer is read directly from the model's distribution over the options, with no generation and no parsing. - **Vision-language**: text and images (tiled for large frames, several images per state) in a single forward pass. - **Audio-language**: text and up to 30 s of speech in a single forward pass. - **Edge-sized**: 587M parameters: a 381M shared trunk and decision head, a 94M vision encoder and a 112M audio encoder. Every modality runs the same trunk weights. Find more information about open d1 in our blog post.
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View details ↗d1-omni-600M-GGUF
LiquidAId1-omni-600M is a decision model developed by Liquid AI: it answers named, typed questions over a state (text, JSON, images or audio) in one forward pass, with no generated tokens. A question is read in one batch, so `-b` and `-ub` must hold it: 4096 covers any image and states of a few thousand tokens, raise them up to 16384 for longer states. The state can be `null` when the images or the audio are the whole state. A request carries images or one audio clip (up to 30 s), not both.
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View details ↗d1-omni-600M-MLX-8bit
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View details ↗d1-omni-600M-MLX-fp16
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View details ↗LFM2-1.2B-Extract
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View details ↗LFM2-1.2B-Extract-GGUF
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View details ↗LFM2-1.2B-GGUF
LiquidAILFM2 is a new generation of hybrid models developed by Liquid AI, specifically designed for edge AI and on-device deployment. It sets a new standard in terms of quality, speed, and memory efficiency.
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View details ↗LFM2-1.2B-RAG
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View details ↗LFM2-1.2B-RAG-GGUF
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View details ↗LFM2-1.2B-Tool
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View details ↗LFM2-1.2B-Tool-GGUF
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View details ↗LFM2-2.6B-Exp
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View details ↗LFM2-2.6B-Exp-GGUF
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View details ↗LFM2-2.6B-GGUF
LiquidAILFM2 is a new generation of hybrid models developed by Liquid AI, specifically designed for edge AI and on-device deployment. It sets a new standard in terms of quality, speed, and memory efficiency.
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View details ↗LFM2-2.6B-Longevity
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