Instructions to use LiquidAI/LFM2.5-VL-450M-Extract with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LiquidAI/LFM2.5-VL-450M-Extract with Transformers:

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

pipe = pipeline("image-text-to-text", model="LiquidAI/LFM2.5-VL-450M-Extract")
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)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("LiquidAI/LFM2.5-VL-450M-Extract")
model = AutoModelForImageTextToText.from_pretrained("LiquidAI/LFM2.5-VL-450M-Extract")
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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LiquidAI/LFM2.5-VL-450M-Extract with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LiquidAI/LFM2.5-VL-450M-Extract"
# 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/LFM2.5-VL-450M-Extract",
		"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/LFM2.5-VL-450M-Extract

SGLang

How to use LiquidAI/LFM2.5-VL-450M-Extract 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/LFM2.5-VL-450M-Extract" \
    --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/LFM2.5-VL-450M-Extract",
		"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/LFM2.5-VL-450M-Extract" \
        --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/LFM2.5-VL-450M-Extract",
		"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/LFM2.5-VL-450M-Extract with Docker Model Runner:
```
docker model run hf.co/LiquidAI/LFM2.5-VL-450M-Extract
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Try LFM • Docs • LEAP • Discord

LFM2.5-VL-450M-Extract

LFM2.5-VL-450M-Extract extracts user-defined fields from images and returns them as JSON. It is Liquid AI's first vision model in the Liquid Nanos collection—compact, task-specific models built for production workflows—and extends the Extract family alongside LFM2-350M-Extract for text documents.

You specify what to extract as a YAML field list in the system prompt, and the model returns a JSON object with those fields. Structured outputs integrate cleanly with rule-based systems and downstream pipelines. Use it out of the box or fine-tune for domain-specific extraction.

⚙️ How it works

System prompt:

wood_color: The overall coloration of the wood surface
wood_texture: The tactile quality of the wood surface 
wood_pattern: The partern types visible on the wood surface

User prompt:
Output:

{
  "wood_color": "light to medium brown",
  "wood_texture": "smooth with visible grain",
  "wood_pattern": "parallel, irregular, wavy"
}

Our model supports the enum feature, which lets you provide a list of possible choices alongside the field description as follows, and the model will return one of the listed values as its answer.

System prompt:

wood_color: The overall coloration of the wood surface, such as blue, red, or light tan
wood_texture: The tactile quality of the wood surface, select from smooth, rough, or grainy
wood_pattern: The partern types visible on the wood surface, e.g., straight, wavy, or curly

🌟 Use cases

Detecting safety-critical events in images (e.g. fallen person, fire, leakage) to trigger automated safety systems.
Collecting statistical information about objects across video frames for analytics pipelines.
Auto-tag product images with structured attributes for Retail/E-commerce.

📄 Model details

Property	Detail
Parameters (LM only)	350M
Vision encoder	SigLIP2 (~100M, SigLIP-2 paper)
Backbone layers	hybrid conv+attention
Image input	Single image, dynamic resolution
Context	128,000 tokens
Vocab size	65,536 (text)
Precision	bfloat16
License	LFM Open License v1.0

📊 Performance

We evaluated LFM2.5-VL-450M-Extract on a 2,000-sample benchmark of (image, schema, JSON) triples, with reference labels generated by an ensemble of frontier multimodal models. Predictions are scored on the following three dimensions:

JSON Validity — share of samples producing strict-parseable JSON
Schema Consistency F1 Score — set-level F1 over predicted vs requested field names, macro-averaged across samples
VLM Judge Score — match against the image directly, judged by a separate vision model (Qwen/Qwen3.5-35B-A3B)

Model	Params	JSON Validity	F1 Score	VLM Judge Score
LFM2.5-VL-450M-Extract	0.45B	98.9	98.8	84.5
LFM2.5-VL-450M	0.45B	97.7	93.5	73.4
SmolVLM-500M-Instruct	0.51B	33.0	26.6	12.2
FastVLM-0.5B	0.76B	22.5	19.3	16.3
Qwen3.5-0.8B	0.87B	96.4	96.3	82.3
InternVL3_5-1B	1.06B	98.0	96.5	80.7
MiniCPM-V-4.6	1.30B	61.8	60.4	57.5
(ref) InternVL3_5-2B	2.35B	99.6	99.2	87.7
(ref) Qwen3.5-2B	2.27B	97.9	97.7	89.7
(ref) gemma-4-E2B-it	2.3B	97.4	97.1	84.4

LFM2-VL-450M-Extract outperforms similarly-sized (sub-1B) open-source VLMs on this benchmark and is competitive with models 4× its size.

Reproducing these numbers: The full evaluation pipeline, which includes extraction, VLM judging, and metric aggregation, is bundled in this repository under model_eval/. Setup, configuration, and run instructions are in the folder's README.

Scope: These numbers characterize the model on the input/output form it is designed for: a single input image, a YAML field list as the schema, and a flat JSON object as the output. Performance is not expected to transfer to largely different tasks, e.g. multi-image reasoning or free-form VQA.

The full evaluation pipeline, which includes extraction, LLM/VLM judging, and metric aggregation, is included in this repository under model_eval/. Usage details are in the folder's README.

🏃 How to run

You can run LFM2.5-VL-450M-Extract with Hugging Face transformers v5.1 or newer:

pip install transformers pillow

from transformers import AutoProcessor, AutoModelForImageTextToText
from transformers.image_utils import load_image

model_id = "LiquidAI/LFM2.5-VL-450M-Extract"
model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    device_map="auto",
    dtype="bfloat16",
    trust_remote_code=True,
)
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)

image = load_image("https://huggingface.co/LiquidAI/LFM2.5-VL-450M-Extract/resolve/main/sample_image.png")

fields_yaml = """wood_color: The overall coloration of the wood surface
wood_texture: The tactile quality of the wood surface
wood_pattern: The pattern types visible on the wood surface"""

system_prompt = f"""Extract the following from the image:

{fields_yaml}

Respond with only a JSON object. Do not include any text outside the JSON."""

conversation = [
    {"role": "system", "content": system_prompt},
    {"role": "user",   "content": [{"type": "image", "image": image}]},
]

inputs = processor.apply_chat_template(
    conversation,
    add_generation_prompt=True,
    return_tensors="pt",
    return_dict=True,
    tokenize=True,
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=512, do_sample=False)
response = processor.batch_decode(
    outputs[:, inputs["input_ids"].shape[1]:],
    skip_special_tokens=True,
)[0]
print(response)
# {
#   "wood_color": "light to medium brown",
#   "wood_texture": "smooth with visible grain",
#   "wood_pattern": "parallel, irregular, wavy"
# }

The model is intended for single-turn conversations. We recommend using greedy decoding (temperature=0).

📬 Contact

Got questions or want to connect? Join our Discord community
If you are interested in custom solutions with edge deployment, please contact our sales team.

Citation

@article{liquidai2025lfm2,
 title={LFM2 Technical Report},
 author={Liquid AI},
 journal={arXiv preprint arXiv:2511.23404},
 year={2025}
}

Downloads last month: 59

Safetensors

Model size

0.4B params

Tensor type

BF16

Model tree for LiquidAI/LFM2.5-VL-450M-Extract

Base model

LiquidAI/LFM2.5-350M-Base

Finetuned

LiquidAI/LFM2.5-350M

Finetuned

LiquidAI/LFM2.5-VL-450M

Finetuned

(24)

this model

Quantizations

1 model

Collection including LiquidAI/LFM2.5-VL-450M-Extract

🎯 Liquid Nanos

Collection

Library of task-specific models: https://www.liquid.ai/blog/introducing-liquid-nanos-frontier-grade-performance-on-everyday-devices • 30 items • Updated 1 day ago • 114

Papers for LiquidAI/LFM2.5-VL-450M-Extract

LFM2 Technical Report

Paper • 2511.23404 • Published Nov 28, 2025 • 61

SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

Paper • 2502.14786 • Published Feb 20, 2025 • 165