--- title: d1-Omni-2B canonical_url: "https://www.modelscope.cn/models/LiquidAI/d1-Omni-2B" md_url: "https://www.modelscope.cn/models/LiquidAI/d1-Omni-2B.md" repository: LiquidAI/d1-Omni-2B last_updated: 2026-11-03 license: other model_type: - lfm3_omni architectures: - D1Model base_model: - LiquidAI/LFM3-Omni-2B base_model_relation: finetune parameters: 2.2B tensor_type: - BF16 library_name: - pytorch - transformer - safetensors frameworks: - pytorch language: - en - ar - zh - fr - de - it - ja - ko - pt - es - vi - th - id - hi - ru - pl downloads: 54 stars: 0 tags: - liquid - lfm3 - edge - decision - classification - extraction - calibration - system-one - multimodal - audio - decision-model - custom_code --- # d1-Omni-2B > d1-Omni-2B - LiquidAI 在 ModelScope 开源的模型。d1-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. LiquidAI/d1-Omni-2B 是 ModelScope 魔搭社区上的 2.2B 参数机器学习模型，采用 other 许可，基于 LiquidAI/LFM3-Omni-2B 构建。 - **Repository**: LiquidAI/d1-Omni-2B - **License**: other - **Parameters**: 2.2B - **Base model**: LiquidAI/LFM3-Omni-2B - **Tags**: liquid, lfm3, edge, decision, classification, extraction, calibration, system-one, multimodal, audio, decision-model, custom_code - **Downloads**: 54 - **Stars**: 0 - **Last updated**: 2026-11-03 Source: https://www.modelscope.cn/models/LiquidAI/d1-Omni-2B ---

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# d1-Omni-2B d1-Omni-2B is a **decision model for text, pictures, and audio**, built on [LFM3-Omni-2B](https://huggingface.co/LiquidAI/LFM3-Omni-2B). It returns calibrated answers in one forward pass, without generating tokens. - **Native audio:** clips and spoken questions go through the same pass as text and pictures, and spoken questions score within 0.5 points of written ones on unseen tasks. - **Beyond classification:** it also extracts text, numbers, and dates with their span, locates objects in pictures, and estimates quantities with an interval. - **Millisecond decisions:** 5.8 ms per decision on an NVIDIA H100 and 3.7 ms on an AMD MI325X, or 12.4 ms and 8.2 ms with a 10-second clip. 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-Omni-2B adds audio to [d1-2B](https://huggingface.co/LiquidAI/d1-2B).

Property	Specification
Checkpoint	Post-trained decision model, built on LFM3-Omni-2B
Parameters	2.17B
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, a video, audio clips, and any number of named questions, written or spoken
Output	Typed answers with their probabilities, with no generated tokens or speech
Languages	English, Arabic, Chinese, French, German, Italian, Japanese, Korean, Portuguese, Spanish, Vietnamese, Thai, Indonesian, Hindi, Russian, Polish. Speech covers the same languages except Hindi, Thai, Vietnamese, and Indonesian.
License	LFM Open License v1.0

d1-Omni-2B is designed for decisions that depend on what is heard, such as call and voice-message routing and sound events, and it handles the same text and picture decisions as d1-2B. It is not a chat model and writes no transcript or speech. Long spoken passages lose much of their accuracy, most of all outside Latin-script languages. Without audio, use [d1-2B](https://huggingface.co/LiquidAI/d1-2B): it is stronger on text and single boxes, reports missing values more often, and is better calibrated. ## Inference

d1-Omni-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 numpy as np 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-Omni-2B", trust_remote_code=True, dtype=dtype).to(device) # A recording: three short beeps over quiet noise, 16 kHz mono rate = 16000 clip = np.random.default_rng(0).normal(0, 0.003, 4 * rate).astype(np.float32) for start in (0.5, 1.5, 2.5): clip[int(start * rate):int(start * rate) + 3200] += 0.3 * np.sin(2 * np.pi * 880 * np.arange(3200) / rate) out = model.system_one(None, { "beeps": {"type": "number", "instructions": "How many beeps are there?", "range": [0, 10], "step": 1}, "speech": {"type": "noul", "instructions": "Does someone speak in the recording?"}, # yes/no: P(yes) }, media=[{"type": "audio", "audio": clip, "sampling_rate": rate}]) a = out["answers"] print(a["beeps"]["number"], a["speech"]["noul"] > 0.5) # 3.0 False ``` A clip from a file is `{"type": "audio", "path": "call.wav"}` (16-bit PCM WAV, resampled to 16 kHz). A spoken question takes a clip as its instructions: `{"type": "noul", "instructions": {"type": "audio", "path": "question.wav"}}`.

Text questions

Several named questions over one state, answered in one pass, as with d1-2B: ```python 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", "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"}}, } out = model.system_one("I was charged twice and my parcel never arrived. Please refund one charge.", questions) a = out["answers"] print(round(a["refund"]["noul"], 2), a["team"]["choice"], a["topics"]["choices"]) # 0.94 billing ['billing', 'delivery'] ```

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': 0.27} ```

Calls

Call	What it does
`system_one(state, questions, media=None)`	Named questions over one state, in one pass. `media` lists audio clips (`{"type": "audio", "path": ...}`, or a waveform under `"audio"` with its `"sampling_rate"`), 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-Omni-2B scores above d1-omni-600M on every benchmark, and above d1-3B on our decision board and on 16 of 18 image benchmarks, with 70% of its parameters. d1-2B, which has no audio input, stays ahead on most rows.

Benchmark	d1-Omni 2B	d1-omni 600M	d1 2B	d1 3B
Decisions
Decision Index public suite (v0.3)	49.0	18.3	51.1	49.0
DecisionBench (Hanno Labs) v1	76.4	58.7	77.1	73.1
Decision Bench (decisionbench.ai) v4	84.7	57.9	85.2	83.6
Fast Decisions	56.8	–	58.8	59.3
JevBench v1.4.1	75.3	58.0	77.9	76.2
ImajevBench v2.0-lite	72.3	21.7	75.5	64.0
WorkflowEvals	77.1	48.8	81.6	74.4
Typed Decisions	63.1	57.5	61.3	65.0
Our board (over a hundred public and private benchmarks)	61.8	–	63.2	58.8
Vision
AI2D	71.5	37.1	76.7	79.9
BLINK	63.5	37.7	66.2	59.2
CountQA	45.4	26.8	51.4	42.6
CV-Bench	84.0	40.8	86.1	82.1
ERQA	42.5	27.3	45.8	39.8
HallusionBench	67.5	52.6	72.1	65.3
MMBench	85.2	54.2	87.9	84.9
MME	83.9	61.0	85.4	82.1
MMMU-Pro	24.1	11.9	26.4	18.5
MMRB2	52.6	48.3	54.2	49.5
MMStar	61.3	31.9	67.5	59.9
MMVP	81.3	54.0	82.7	77.0
NaturalBench	80.0	58.8	81.5	79.8
POPE	89.9	82.7	88.7	88.6
V*	76.4	35.6	76.4	73.8
VisualPuzzles	26.9	26.2	29.9	29.5
VisualWebBench	88.6	14.9	96.2	71.4
VL-RewardBench	68.8	48.1	69.3	65.0
Average	67.7	42.8	70.2	65.4

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.

### Audio Each audio row is scored heard and written, on clips and voices that no training row has. Scores are effective accuracy, where 0 is chance and 100 is every answer right. In the second group, synthetic voices read the question and short state of 1,284 test rows aloud, in each row's language.

Test	Written	Heard
Held-out audio
Spoken questions, on tasks absent from training	45.2	44.7
Spoken short states	42.0	22.8
Assistant commands, real speakers (SLURP intents)	87.4	81.7
Assistant commands in 12 languages (Speech-MASSIVE intents)	85.2	63.4
Spoken language, 43 languages (FLEURS)	92.4	92.5
Sound events (ESC-50)	–	84.5
Decision suites read aloud
DecisionBench (Hanno Labs) v1, routing and triage	83.4	78.2
JevBench v1.4.1, the rows a voice can say	87.6	89.0
Abstention: none of the options applies (our set)	78.5	75.8
Typed Decisions, customer service	50.5	48.0
Belebele, passages in 8 languages	83.6	41.8

### 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	10 s audio clip	64 states in one call
NVIDIA H100 PCIe	5.8 ms	7.7 ms	52 ms	12.1 ms	12.4 ms	1,154 / s
AMD MI325X	3.7 ms	5.6 ms	43 ms	8.6 ms	8.2 ms	1,697 / s
Apple M5 Pro (`mps`)	21.1 ms	28.9 ms	694 ms	40.2 ms	33.3 ms	110 / 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).