--- library_name: transformers license: other license_name: lfm1.0 license_link: LICENSE pipeline_tag: image-text-to-text tags: - liquid - lfm3 - edge - decision - classification - extraction - calibration - system-one - multimodal - decision-model - custom_code language: - en - ar - zh - fr - de - it - ja - ko - pt - es - vi - th - id - hi - ru - pl ---
Liquid AI
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# d1-2B d1-2B is a **decision model for text, pictures, and video**, built on [LFM3-VL-2B](https://huggingface.co/LiquidAI/LFM3-VL-2B). It returns calibrated answers in one forward pass, without generating tokens. - **Best-in-class:** it leads the open d1 models on average and on our board of over a hundred decision benchmarks, ahead of d1-3B at under two thirds of its size. - **Beyond classification:** it also extracts text, numbers, and dates with their span, locates objects in pictures, and estimates quantities with an interval. - **Millisecond decisions:** 5.7 ms per decision on an NVIDIA H100 and 3.8 ms on an AMD MI325X, with any number of questions about one state in one call. Find more information about open d1 in [our release blog post](https://www.liquid.ai/blog/open-d1). ## Model details
The [LFM3 family](https://huggingface.co/collections/LiquidAI/lfm3) includes base, text, vision, audio, omni, and decision models. d1-2B decides over text, pictures, and video, and [d1-Omni-2B](https://huggingface.co/LiquidAI/d1-Omni-2B) adds audio.
Property Specification
Checkpoint Post-trained decision model, built on LFM3-VL-2B
Parameters 1.92B
Architecture LFM3 backbone, 24 layers: 13 gated short convolutions, 5 sliding-window attention, 6 global attention
Context length 128K tokens
Vocabulary 128,000 tokens
Input A state (text or JSON), pictures or a video, and any number of named questions
Output Typed answers with their probabilities, with no generated tokens
Languages English, Arabic, Chinese, French, German, Italian, Japanese, Korean, Portuguese, Spanish, Vietnamese, Thai, Indonesian, Hindi, Russian, Polish
License LFM Open License v1.0
d1-2B is designed for any pipeline step that needs a decision: routing and triage, moderation and guardrails, intent and topic classification, extraction from documents and forms, judging and reranking, and visual inspection. It is not a chat model. Knowledge-heavy questions favor larger models, and answers get less reliable past about 64K tokens of state. Estimates are reliable on counts and scores, less so on free quantities. ## Inference
d1-2B runs in [Transformers](https://docs.liquid.ai/deployment/gpu-inference/transformers) with its own model code. It reads its answers from a single forward pass, so there are no sampling parameters to set. **Transformers quick start:** ```bash pip install "transformers>=5.19" torch torchvision pillow ``` ```python import torch from transformers import AutoModel 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/d1-2B", trust_remote_code=True, dtype=dtype).to(device) # Several named questions over one state, answered in one pass questions = { "refund": {"type": "noul", "instructions": "Is the customer asking for a refund?"}, # yes/no: P(yes) "team": {"type": "choice", "instructions": "Which team should handle this?", "criteria": {"billing": "Charges, refunds, invoices", "shipping": "Delivery and parcels", "technical": "App or site faults"}}, "topics": {"type": "choice", "multiple": True, "instructions": "Which topics does the message raise?", "criteria": {"billing": "Payments and charges", "delivery": "Shipping and parcels", "login": "Signing in to the account"}}, "urgency": {"type": "score", "instructions": "How urgent is this ticket?", "criteria": ["low", "medium", "high"]}, } out = model.system_one("I was charged twice and my parcel never arrived. Please refund one charge.", questions) a = out["answers"] urgency = a["urgency"]["legend"][str(int(a["urgency"]["score"]))] print(round(a["refund"]["noul"], 2), a["team"]["choice"], a["topics"]["choices"], urgency) # 1.0 billing ['billing', 'delivery'] medium ```
Values from a document ```python invoice = "ACME SA\nInvoice 2026-117\nTotal due: 1 234,50 EUR\nPay before 03/04/2026 (day/month/year)" out = model.system_one(invoice, { "payee": {"type": "text", "instructions": "Who issued the invoice?"}, "total": {"type": "number", "instructions": "How much is due?"}, "due": {"type": "date", "instructions": "By when must it be paid?"}, "vat": {"type": "number", "instructions": "How much VAT is charged?"}, }) a = out["answers"] print(a["payee"]["text"], a["total"]["number"], a["due"]["date"], a["vat"]["number"]) # ACME SA 1234.5 2026-04-03 None ```
Pictures and boxes Count, locate one object, or list every instance (`max` reads at most that many): ```python from transformers.image_utils import load_image image = load_image("http://images.cocodataset.org/val2017/000000039769.jpg") # two cats on a sofa out = model.system_one(None, { "cats": {"type": "number", "instructions": "How many cats are there?", "range": [0, 10], "step": 1}, "remote": {"type": "box", "instructions": "Where is the remote control on the left?"}, "every_cat": {"type": "boxes", "instructions": "Every cat.", "max": 4}, }, media=[image]) a = out["answers"] print(a["cats"]["number"], a["every_cat"]["count"], {k: round(v, 2) for k, v in a["remote"]["box"].items()}) # 2.0 2 {'top': 0.15, 'bottom': 0.24, 'left': 0.06, 'right': 0.28} ```
Many requests in one call ```python tickets = ["Where is my parcel? It was due Monday.", "The app crashes when I open settings."] for out in model.system_one_batch([(t, {"team": questions["team"]}) for t in tickets]): print(out["answers"]["team"]["choice"]) # shipping # technical ```
Calls
Call What it does
system_one(state, questions, media=None) Named questions over one state, in one pass. media lists pictures (PIL images or {"type": "image", "path": ...}) and videos as their frames ({"type": "video", "video": [PIL images], "fps": 24}).
system_one_batch([(state, questions[, media]), ...]) Many requests in one call, batched.
set_size= (either call) Questions read together after the state. The default is the model's trained size (16). Larger is faster and costs some accuracy on text.
A state is a string, any JSON value, or `None` when the media are the whole state. Each call returns `{"answers": {name: answer}, "usage": {"input_tokens": n, "output_tokens": 0}}`.
Question types
type Question Answer
noul Yes or no noul: P(yes)
choice One of the options criteria names ({name: description}) choice, confidence, probabilities
choice with multiple: true Any of the options criteria names choices, probabilities (each option's own P(yes))
score One of 2 to 10 ordered levels, with criteria listing them lowest first score (the median level), confidence, probabilities, legend
text, number, date A value to read from the state The field of its type (a number with its unit, a date in ISO 8601), span, found
number with range An estimate, step apart number (the median), interval (p10, p90)
box, boxes One object, or every instance (16 at most, or max), in the one picture box (top, bottom, left, right as fractions of the picture), found, confidence, or boxes and count
A value or a box is missing when `found` is under 0.5, and it is then `None`. Calibrate any decision threshold on data from your own use.
## Performance
### Benchmarks d1-2B leads the open d1 models on our decision board, on average, and on most single benchmarks, ahead of d1-3B with under two thirds of its parameters.
Benchmark d1
2B		 d1-omni
600M		 d1-Omni
2B		 d1
3B
Decisions
Decision Index public suite (v0.3) 51.1 18.3 49.0 49.0
DecisionBench (Hanno Labs) v1 77.1 58.7 76.4 73.1
Decision Bench (decisionbench.ai) v4 85.2 57.9 84.7 83.6
Fast Decisions 58.8 – 56.8 59.3
JevBench v1.4.1 77.9 58.0 75.3 76.2
ImajevBench v2.0-lite 75.5 21.7 72.3 64.0
WorkflowEvals 81.6 48.8 77.1 74.4
Typed Decisions 61.3 57.5 63.1 65.0
Our board (over a hundred public and private benchmarks) 63.2 – 61.8 58.8
Vision
AI2D 76.7 37.1 71.5 79.9
BLINK 66.2 37.7 63.5 59.2
CountQA 51.4 26.8 45.4 42.6
CV-Bench 86.1 40.8 84.0 82.1
ERQA 45.8 27.3 42.5 39.8
HallusionBench 72.1 52.6 67.5 65.3
MMBench 87.9 54.2 85.2 84.9
MME 85.4 61.0 83.9 82.1
MMMU-Pro 26.4 11.9 24.1 18.5
MMRB2 54.2 48.3 52.6 49.5
MMStar 67.5 31.9 61.3 59.9
MMVP 82.7 54.0 81.3 77.0
NaturalBench 81.5 58.8 80.0 79.8
POPE 88.7 82.7 89.9 88.6
V* 76.4 35.6 76.4 73.8
VisualPuzzles 29.9 26.2 26.9 29.5
VisualWebBench 96.2 14.9 88.6 71.4
VL-RewardBench 69.3 48.1 68.8 65.0
Average70.242.867.765.4
Evaluation details

All four models are scored on the same items with the same scoring, so a few of d1-3B's and d1-omni-600M's numbers differ slightly from their own cards. d1-omni-600M has no Fast Decisions or board score. Average is the unweighted mean of the public benchmarks that all four models have, so it leaves out Fast Decisions and our board. Image benchmarks are read as decisions over each benchmark's options, on at most 1,000 rows each. Three of them are saturated: POPE, MME, and VisualWebBench.

### Speed Warm calls after `model.compile()` in bf16, median of 20 calls, and best of three runs on the H100 and the M5 Pro. Compiling takes about a minute on the first calls.
Hardware One question 3 questions 5K-token state 384 px picture Box Boxes 64 states in one call
NVIDIA H100 PCIe 5.7 ms 7.6 ms 52 ms 12.3 ms 16.1 ms 24.4 ms 1,132 / s
AMD MI325X 3.8 ms 5.6 ms 44 ms 8.7 ms 11.2 ms 16.5 ms 1,699 / s
Apple M5 Pro (mps) 18.6 ms 26.5 ms 753 ms 45.2 ms 53.5 ms 100 ms 109 / s
## 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/open-d1}, } ``` ```bibtex @article{liquidAI2026LFM3, author = {Liquid AI}, title = {Introducing LFM3: Every Modality for Specialized Intelligence}, journal = {Liquid AI Blog}, year = {2026}, note = {https://www.liquid.ai/blog/lfm3}, } ``` For questions and feedback, join [Discord](https://discord.com/invite/liquid-ai). For custom deployment and fine-tuning support, [contact Liquid AI](https://www.liquid.ai/contact).