--- library_name: transformers license: other license_name: lfm1.0 license_link: LICENSE language: - ar - zh - en - fr - de - hi - id - it - ja - ko - pl - pt - ru - es - th - vi pipeline_tag: image-text-to-text base_model: LiquidAI/LFM2.5-VL-3B tags: - liquid - lfm2.5 - edge - decision - classification - calibration - system-one - multimodal - decision-model ---
Liquid AI
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# d1-3B d1-3B is a 3B parameter **decision model** built on [LFM2.5-VL-3B](https://huggingface.co/LiquidAI/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](https://www.liquid.ai/blog/open-d1). ![image](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/r2H7UlZ_m48SYAhZWIHiu.png) > [!NOTE] > 💻 **Demos**: Try d1-3B in a Hugging Face space without any setup: > [**Open d1 Arcade**](https://huggingface.co/spaces/LiquidAI/system-one-arcade): Collection of 10 demos using d1-3B ## 🗒️ Model Details | Model | Parameters | Description | |---|---|---| | [LFM2.5-VL-3B](https://huggingface.co/LiquidAI/LFM2.5-VL-3B) | 3.1B | General-purpose vision-language model (base) | | **[d1-3B](https://huggingface.co/LiquidAI/d1-3B)** | 3.1B | Post-trained for single-pass, calibrated decisions | d1-3B is a multimodal decision model with the following features: - **Total parameters**: 3.12B - **Vision encoder**: SigLIP2 NaFlex shape-optimized 400M - **Context length**: 32,768 tokens - **Vocabulary size**: 128,000 We recommend d1-3B wherever a pipeline needs a yes/no, a pick from named options, or a rating: routing and triage, moderation, intent and topic classification, extraction checks, reranking, LLM-as-a-judge scoring, agent guardrails, and visual inspection. It is not a chat model and does not write text. ## 🏃 How to use Install the dependencies (requires `transformers>=5.14`): ```bash pip install "transformers>=5.14" torch torchvision pillow ``` The model ships its own code, so load it with `trust_remote_code=True`: ```python import torch from transformers import AutoModel from transformers.image_utils import load_image device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu" dtype = torch.float32 if device == "cpu" else torch.bfloat16 model = AutoModel.from_pretrained("LiquidAI/d1-3B", trust_remote_code=True, dtype=dtype).to(device) # Text: several named questions over one state, answered in one pass questions = { "refund": { "type": "noul", "instructions": "Is the customer asking for a refund?", }, "team": { "type": "choice", "instructions": "Which team should handle this?", "criteria": { "billing": "Charges, refunds, invoices", "technical": "App or site faults", "fraud": "Suspected unauthorised use", }, }, "urgency": { "type": "score", "instructions": "How urgent is this?", "criteria": ["Can wait", "Today", "Blocking the customer now"], }, } print(model.system_one("I was charged twice this month, please refund one of them.", questions)) # Image: the photo is the whole state image = load_image("http://images.cocodataset.org/val2017/000000039769.jpg") # two cats on a sofa cats = { "type": "choice", "instructions": "How many cats are there?", "criteria": {"one": "One", "two": "Two", "more": "Three or more"}, } print(model.system_one(None, {"cats": cats}, images=[image])) # Batch: many requests, packed together with no padding tickets = ["Where is my parcel? It was due Monday.", "The app crashes when I open settings."] print(model.system_one_batch([(t, {"team": questions["team"]}) for t in tickets])) ``` | call | | |---|---| | `system_one(state, questions, images=None)` | Named questions over one state, in one pass. The state and its images are read once for all questions. | | `system_one_batch([(state, questions[, images]), ...])` | Many requests, packed with no padding. | A state is a string, any JSON value, or `None` when the images are the whole state. ### Questions and answers Questions follow the Decision Index schema: `type`, `instructions`, and `criteria`. | `type` | `criteria` | answer fields | |---|---|---| | `noul`: yes or no | optional: `{"true": "...", "false": "..."}` to define each side | `noul`: P(yes) | | `choice`: one of named options | `{name: description}` | `choice`, `confidence`, `probabilities` | | `score`: 2 to 10 ordered levels | a list of level descriptions, lowest first | `score` (the expected level), `confidence`, `probabilities`, `legend` | Each call returns `{"answers": {name: answer}, "usage": {"input_tokens": n, "output_tokens": 0}}`. ## ⚡ Speed Warm calls, one request at a time: a single question, three questions over one state, a 3.4k-token state and a 384 px image. The last column is throughput with 64 states packed into one pass. ### Edge Inference We measure latency on an Apple M5 Pro and, in collaboration with NVIDIA, on an NVIDIA Jetson AGX Thor, a Jetson AGX Orin 64 GB and a Jetson Orin Nano. | | one question | 3 questions, one pass | 3.4k-token state | 384 px image | 64 states, packed | |---|---:|---:|---:|---:|---:| | Apple M5 Pro (`mps`) | 30 ms | 41 ms | 640 ms | 62 ms | 78 / s | | NVIDIA Jetson AGX Thor | 16 ms | 20 ms | 220 ms | 35 ms | 262 / s | | NVIDIA Jetson AGX Orin 64 GB | 26 ms | 35 ms | 560 ms | 83 ms | 110 / s | | NVIDIA Jetson Orin Nano | 50 ms | 73 ms | 1640 ms | 202 ms | 38 / s | ### GPU Inference We measure latency on an NVIDIA RTX 4090 and an AMD MI325X, in bf16, median of 20 runs. | | one question | 3 questions, one pass | 3.4k-token state | 384 px image | 64 states, packed | |---|---:|---:|---:|---:|---:| | NVIDIA RTX 4090 | 8 ms | 21 ms | 102 ms | 17 ms | 475 / s | | AMD MI325X | 9 ms | 14 ms | 44 ms | 18 ms | 1,106 / s | On NVIDIA GPUs, `model.compile(mode="reduce-overhead")` runs single questions as CUDA graphs (the RTX 4090 row uses it). Without it, a single question takes 16 ms. The first call with a new shape pays for kernel selection or compilation, so warm up the shapes you serve. ## 📊 Performance All results are on public benchmarks. ### Decision Index 0.2.1 We scored d1-3B with the official scorer (not a leaderboard submission). All other rows come from the public leaderboard v0.2.1. | Model | Size | Decision Index | Knowledge | Language | Retrieval | Tools | Arts | |---|---:|---:|---:|---:|---:|---:|---:| | Winnow-12B | 12B | 50.02 | 33.8 | 56.0 | 54.0 | 71.0 | 30.0 | | **d1-3B** | **3B** | **48.57** | 23.8 | 56.4 | 52.8 | **74.5** | **36.3** | | Decider 35B-A3B | 36B | 47.11 | 31.8 | 55.5 | 54.7 | 56.5 | 32.6 | | JPT-9B | 9.7B | 46.89 | 31.7 | 56.7 | 44.6 | 67.0 | 28.6 | | Decision 1.0 Lux | 9.7B | 43.49 | 30.9 | 48.0 | 50.0 | 57.2 | 26.4 | | JPT-4B | 4.7B | 43.04 | 28.7 | 52.5 | 45.0 | 57.2 | 25.8 | | Jet v6.2 | 4.7B | 42.60 | 28.7 | 43.9 | 48.2 | 62.9 | 27.0 | | Decider 4B | 4.7B | 40.70 | 25.7 | 46.0 | 44.7 | 58.6 | 25.0 | | Winnow-E4B | 8.0B | 39.89 | 22.3 | 45.1 | 43.8 | 62.5 | 22.8 | | Decider 2B | 2.3B | 28.97 | 14.9 | 32.6 | 37.3 | 42.4 | 14.6 | ### Benchmarks as decisions Besides the Decision Index, we added a few other internal evaluations based on public benchmarks. | Benchmark | d1-3B | Decider 4B | Decider 2B | |---|---:|---:|---:| | SQuAD 2.0 | **85.3** | 76.0 | 67.7 | | Civil Comments | 93.0 | 92.8 | **93.6** | | MASSIVE intent | 87.3 | **88.3** | 81.1 | | HelpSteer2 | 36.7 | **42.0** | 32.0 | | PubMedQA | **66.0** | 63.3 | 65.7 | | BoolQ | 86.7 | **89.0** | 87.3 | | XNLI | 85.0 | **88.6** | 85.0 | | PAWS-X | **76.9** | 69.8 | 59.5 | | **Mean** | **77.1** | 76.2 | 71.5 | d1-3B also scores 71.8 on [DecisionBench](https://huggingface.co/datasets/Hanno-Labs/decision-bench) (eng v1, all 23,900 rows) and 69.3 on [Fast Decisions](https://huggingface.co/datasets/fastino/fast-decisions) (dev split). ### Vision Eleven public image benchmarks, read as decisions over each benchmark's options (at most 1,000 rows each), compared with the base model: | Benchmark | d1-3B | LFM2.5-VL-3B | |---|---:|---:| | AI2D | 79.9 | 80.9 | | BLINK | 59.2 | 58.7 | | CV-Bench | 82.1 | 87.6 | | HallusionBench | 65.3 | 65.0 | | MMBench | 84.9 | 84.3 | | MME | 82.1 | 82.4 | | MMStar | 59.9 | 61.2 | | MMVP | 77.0 | 73.7 | | POPE | 88.5 | 90.1 | | VisualWebBench | 71.4 | 78.3 | | VL-RewardBench | 65.0 | 50.9 | | **Mean** | **74.1** | 73.9 | | [ImajevBench](https://huggingface.co/datasets/mohit67890/imajev-bench) (dev and calibration, 253 rows) | 64.0 | 66.8 | With the images removed, the same questions score 45.1, so the answers come from the images. ## 📬 Contact - Got questions or want to connect? [Join our Discord community](https://discord.com/invite/liquid-ai) - If you are interested in custom solutions with edge deployment, please contact [our sales team](https://www.liquid.ai/contact). ## Citation ```bibtex @article{liquidAI2026opend1, author = {Liquid AI}, title = {Open d1: Edge decision models for text, vision, and audio}, journal = {Liquid AI Blog}, year = {2026}, note = {https://www.liquid.ai/blog/d1-open}, } ``` ```bibtex @article{liquidai2025lfm2, title = {LFM2 Technical Report}, author = {Liquid AI}, journal = {arXiv preprint arXiv:2511.23404}, year = {2025} } ```