LFM2.5-VL-1.6B

Best-in-class 1.6B vision-language model for multimodal understanding

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LFM2.5-VL-1.6B is Liquid AI’s flagship vision-language model, delivering exceptional performance on image understanding, visual reasoning, and multimodal tasks. Built on LFM2.5 with a dynamic SigLIP2 image encoder.

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Specifications

Property	Value
Parameters	1.6B
Context Length	32K tokens
Architecture	LFM2.5-VL (Dense)

Image Captioning

Detailed descriptions and alt-text

Visual Reasoning

Scene understanding and visual Q&A

OCR & Extraction

Text recognition and document parsing

Quick Start

Transformers

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/LFM2.5-VL-1.6B"
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)

vLLM

vLLM support for LFM Vision Models requires a specific version. Install from the custom source below.

Install:

VLLM_PRECOMPILED_WHEEL_COMMIT=72506c98349d6bcd32b4e33eec7b5513453c1502 \
  VLLM_USE_PRECOMPILED=1 \
  pip install git+https://github.com/vllm-project/vllm.git

pip install git+https://github.com/huggingface/transformers.git@3c2517727ce28a30f5044e01663ee204deb1cdbe pillow

Run:

from vllm import LLM, SamplingParams

IMAGE_URL = "http://images.cocodataset.org/val2017/000000039769.jpg"

llm = LLM(
    model="LiquidAI/LFM2.5-VL-1.6B",
    max_model_len=1024,
)

sampling_params = SamplingParams(
    temperature=0.0,
    max_tokens=256,
)

messages = [{
    "role": "user",
    "content": [
        {"type": "image_url", "image_url": {"url": IMAGE_URL}},
        {"type": "text", "text": "Describe what you see in this image."},
    ],
}]

outputs = llm.chat(messages, sampling_params)
print(outputs[0].outputs[0].text)