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
```

EdoardoMosca

iamleonie commited on 23 days ago

Commit

1ff39df

1 Parent(s): 60a46c3

Update README.md (#2)

Browse files

- Update README.md (d74b5f7ad717c4f78cbdbfdc27c1e79b2afc493e)

Co-authored-by: Leonie Monigatti <iamleonie@users.noreply.huggingface.co>

Files changed (1) hide show

README.md +25 -4

README.md CHANGED Viewed

@@ -3,6 +3,23 @@ library_name: transformers
 license: other
 license_name: lfm1.0
 license_link: LICENSE
 pipeline_tag: image-text-to-text
 base_model: LiquidAI/LFM2.5-VL-3B
 tags:
@@ -25,14 +42,13 @@ tags:
   <div style="display: flex; justify-content: center; gap: 0.5em; margin-bottom: 1em;">
     <a href="https://playground.liquid.ai/"><strong>Try LFM</strong></a> •
     <a href="https://docs.liquid.ai/lfm/getting-started/welcome"><strong>Docs</strong></a> •
-    <a href="https://leap.liquid.ai/"><strong>LEAP</strong></a> •
     <a href="https://discord.com/invite/liquid-ai"><strong>Discord</strong></a>
   </div>
 </div>
 # d1-3B
-d1-3B is a 3B **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 named questions. It returns calibrated,
 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.
@@ -43,10 +59,13 @@ model's distribution over the options, with no generation and no parsing.
   (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.
-<video src="https://huggingface.co/LiquidAI/d1-3b-RC/resolve/main/assets/d1-3b-smash.mp4" controls width="100%"></video>
 ![Decision Index against model size](assets/di_pareto.png)
 ## 🗒️ Model Details
 | Model | Parameters | Description |
@@ -54,6 +73,8 @@ model's distribution over the options, with no generation and no parsing.
 | [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-RC)** | 3.1B | Post-trained for single-pass, calibrated decisions |
 - **Total parameters**: 3.12B
 - **LM backbone**: LFM2.5-2.6B, 30 layers (22 double-gated short convolution blocks + 8 GQA)
 - **Vision encoder**: SigLIP2 NaFlex shape-optimized 400M

 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:
   <div style="display: flex; justify-content: center; gap: 0.5em; margin-bottom: 1em;">
     <a href="https://playground.liquid.ai/"><strong>Try LFM</strong></a> •
     <a href="https://docs.liquid.ai/lfm/getting-started/welcome"><strong>Docs</strong></a> •
     <a href="https://discord.com/invite/liquid-ai"><strong>Discord</strong></a>
   </div>
 </div>
 # 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 named questions. It returns calibrated,
 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.
   (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.
 ![Decision Index against model size](assets/di_pareto.png)
+> [!NOTE]
+> 💻 **Demos**: Try d1-3B in a Hugging Face space without any setup:
+> **TODO: add demo**
 ## 🗒️ 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-RC)** | 3.1B | Post-trained for single-pass, calibrated decisions |
+d1-3B is a multimodal decision model with the following features:
 - **Total parameters**: 3.12B
 - **LM backbone**: LFM2.5-2.6B, 30 layers (22 double-gated short convolution blocks + 8 GQA)
 - **Vision encoder**: SigLIP2 NaFlex shape-optimized 400M