Instructions to use LiquidAI/d1-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LiquidAI/d1-3B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="LiquidAI/d1-3B", trust_remote_code=True)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("LiquidAI/d1-3B", trust_remote_code=True)
model = AutoModelForMultimodalLM.from_pretrained("LiquidAI/d1-3B", trust_remote_code=True, device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LiquidAI/d1-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LiquidAI/d1-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LiquidAI/d1-3B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/LiquidAI/d1-3B

SGLang

How to use LiquidAI/d1-3B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "LiquidAI/d1-3B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LiquidAI/d1-3B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "LiquidAI/d1-3B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LiquidAI/d1-3B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use LiquidAI/d1-3B with Docker Model Runner:
```
docker model run hf.co/LiquidAI/d1-3B
```

Aurelien-Lac commited on Oct 7

Commit

90122b1

verified ·

1 Parent(s): e276b8f

Update benchmark numbers

Browse files

Files changed (1) hide show

README.md +28 -28

README.md CHANGED Viewed

@@ -53,9 +53,9 @@ You give it a state (text, JSON, images, or a mix) and a set of named questions.
 typed answers in **one forward pass with zero output tokens**: every answer is read directly from the
 model's distribution over the options, with no generation and no parsing.
-- **Best decision model under 10B on the Decision Index 0.2.1** (leaderboard of 2026-09-28): 47.12, ahead
-  of every 4B and 9B model and level with Decider 35B-A3B (47.11).
-- **Multimodal**: images and text in the same state. It scores 73.7 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.
@@ -180,7 +180,7 @@ scorer; it is not a leaderboard submission. All other rows come from the public
 | 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** | **47.12** | 27.8 | 53.5 | 52.2 | 66.7 | **34.6** |
 | 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 |
@@ -197,18 +197,18 @@ decision protocol, not each benchmark's own.
 | Benchmark | d1-3B | Decider 4B | Decider 2B |
 |---|---:|---:|---:|
-| SQuAD 2.0 | **83.3** | 76.0 | 67.7 |
-| Civil Comments | 93.3 | 92.8 | **93.6** |
-| MASSIVE intent | 86.9 | **88.3** | 81.1 |
-| HelpSteer2 | 38.0 | **42.0** | 32.0 |
-| PubMedQA | **68.3** | 63.3 | 65.7 |
-| BoolQ | 86.3 | **89.0** | 87.3 |
-| XNLI | 85.6 | **88.6** | 85.0 |
-| PAWS-X | **76.4** | 69.8 | 59.5 |
-| **Mean** | **77.3** | 76.2 | 71.5 |
-d1-3B also scores 69.5 on [DecisionBench](https://huggingface.co/datasets/Hanno-Labs/decision-bench) (eng v1,
-all 23,900 rows) and 68.8 on [Fast Decisions](https://huggingface.co/datasets/fastino/fast-decisions)
 (dev split).
 ### Vision
@@ -218,19 +218,19 @@ compared with the base model:
 | Benchmark | d1-3B | LFM2.5-VL-3B |
 |---|---:|---:|
-| AI2D | 80.1 | 80.9 |
-| BLINK | 58.2 | 58.7 |
-| CV-Bench | 82.7 | 87.6 |
-| HallusionBench | 63.8 | 65.0 |
-| MMBench | 84.3 | 84.3 |
-| MME | 81.7 | 82.4 |
-| MMStar | 59.4 | 61.2 |
-| MMVP | 76.3 | 73.7 |
 | POPE | 88.5 | 90.1 |
-| VisualWebBench | 71.6 | 78.3 |
-| VL-RewardBench | 63.6 | 50.9 |
-| **Mean** | **73.7** | 73.9 |
-| [ImajevBench](https://huggingface.co/datasets/mohit67890/imajev-bench) (dev and calibration, 253 rows) | 66.8 | 66.8 |
 With the images removed, the same questions score 45.1, so the answers come from the images.

 typed answers in **one forward pass with zero output tokens**: every answer is read directly from the
 model's distribution over the options, with no generation and no parsing.
+- **Best decision model under 10B on the Decision Index 0.2.1** (leaderboard of 2026-09-28): 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.
 | 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 |
 | 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
 | 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.