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← Back to Vision Models LFM2-VL-450M is Liquid AI’s smallest vision-language model, designed for edge deployment with strict memory and compute constraints. Delivers fast multimodal inference on resource-limited devices.

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Specifications

PropertyValue
Parameters450M
Context Length32K tokens
ArchitectureLFM2-VL (Dense)

Ultra-Light

Minimal memory footprint

Low Latency

Fastest vision model inference

Edge-Ready

Runs on mobile and embedded devices

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Quick Start

Install:
pip install git+https://github.com/huggingface/transformers.git@3c2517727ce28a30f5044e01663ee204deb1cdbe pillow torch
Download & Run:
from transformers import AutoProcessor, AutoModelForImageTextToText
from transformers.image_utils import load_image

model_id = "LiquidAI/LFM2-VL-450M"
model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype="bfloat16"
)
processor = AutoProcessor.from_pretrained(model_id)

url = "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
image = load_image(url)

conversation = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": image},
            {"type": "text", "text": "What is in this 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=256)
response = processor.batch_decode(outputs, skip_special_tokens=True)[0]
print(response)