LFM2.5-VL

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LFM2.5‑VL-1.6B

LFM2.5‑VL-1.6B is Liquid AI's refreshed version of the first vision-language model, LFM2-VL-1.6B, built on an updated backbone LFM2.5-1.2B-Base and tuned for stronger real-world performance. Find more about LFM2.5 family of models in our blog post.

• Enhanced instruction following on vision and language tasks.
• Improved multilingual vision understanding in Arabic, Chinese, French, German, Japanese, Korean, and Spanish.
• Robust understanding of visual content with improved results on multi-image inputs, high-resolution images, and OCR.

🎥⚡️ You can try LFM2.5-VL-1.6B running locally in your browser with our real-time video stream captioning WebGPU demo 🎥⚡️

Alternatively, try the API model on the Playground.

📄 Model details

LFM2.5-VL-1.6B is a general-purpose vision-language model with the following features:

• LM Backbone: LFM2.5-1.2B-Base
• Vision encoder: SigLIP2 NaFlex shape‑optimized 400M
• Context length: 32,768 tokens
• Vocabulary size: 65,536
• Languages: English, Arabic, Chinese, French, German, Japanese, Korean, and Spanish
• Native resolution processing: handles images up to 512*512 pixels without upscaling and preserves non-standard aspect ratios without distortion
• Tiling strategy: splits large images into non-overlapping 512×512 patches and includes thumbnail encoding for global context
• Inference-time flexibility: user-tunable maximum image tokens and tile count for speed/quality tradeoff without retraining
• Generation parameters:
  • text: temperature=0.1, min_p=0.15, repetition_penalty=1.05
  • vision: min_image_tokens=64 max_image_tokens=256, do_image_splitting=True

We recommend using it for general vision-language workloads, OCR or document comprehension. It’s not well-suited for knowledge-intensive tasks.

Chat Template

LFM2.5-VL uses a ChatML-like format. See the Chat Template documentation for details.

You are a helpful multimodal assistant by Liquid AI.
This image shows a Caenorhabditis elegans (C. elegans) nematode.

You can use processor.apply_chat_template() to format your messages automatically.

🏃 Inference

You can run LFM2.5-VL-1.6B with Hugging Face transformers v5.1 or newer:

pip install transformers pillow

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

# Load model and processor
model_id = "LiquidAI/LFM2.5-VL-1.6B"
model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    device_map="auto",
    dtype="bfloat16"
)
processor = AutoProcessor.from_pretrained(model_id)

# Load image and create conversation
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?"},
        ],
    },
]

# Generate Answer
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=64)
processor.batch_decode(outputs, skip_special_tokens=True)[0]

# This image showcases the iconic Statue of Liberty standing majestically on Liberty Island in New York Harbor. The statue is positioned on a small island surrounded by calm blue waters, with the New York City skyline visible in the ba