LFM2‑VL

LFM2‑VL is Liquid AI's first series of multimodal models, designed to process text and images with variable resolutions. Built on the LFM2 backbone, it is optimized for low-latency and edge AI applications.

We're releasing the weights of two post-trained checkpoints with 450M (for highly constrained devices) and 1.6B (more capable yet still lightweight) parameters.

• 2× faster inference speed on GPUs compared to existing VLMs while maintaining competitive accuracy
• Flexible architecture with user-tunable speed-quality tradeoffs at inference time
• Native resolution processing up to 512×512 with intelligent patch-based handling for larger images, avoiding upscaling and distortion

Find more about our vision-language model in the LFM2-VL post and its language backbone in the LFM2 blog post.

📄 Model details

Due to their small size, we recommend fine-tuning LFM2-VL models on narrow use cases to maximize performance. They were trained for instruction following and lightweight agentic flows. Not intended for safety‑critical decisions.

Supported languages: English

Generation parameters: We recommend the following 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

Chat template: LFM2-VL uses a ChatML-like chat template as follows:

<|startoftext|><|im_start|>system
You are a helpful multimodal assistant by Liquid AI.<|im_end|>
<|im_start|>user
<image>Describe this image.<|im_end|>
<|im_start|>assistant
This image shows a Caenorhabditis elegans (C. elegans) nematode.<|im_end|>

Images are referenced with a sentinel (<image>), which is automatically replaced with the image tokens by the processor.

You can apply it using the dedicated .apply_chat_template() function from Hugging Face transformers.

Architecture

• Hybrid backbone: Language model tower (LFM2-1.2B or LFM2-350M) paired with SigLIP2 NaFlex vision encoders (400M shape-optimized or 86M base variant)
• 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 (in 1.6B model)
• Efficient token mapping: 2-layer MLP connector with pixel unshuffle reduces image tokens (e.g., 256×384 image → 96 tokens, 1000×3000 → 1,020 tokens)
• Inference-time flexibility: User-tunable maximum image tokens and patch count for speed/quality tradeoff without retraining

Training approach

• Builds on the LFM2 base model with joint mid-training that fuses vision and language capabilities using a gradually adjusted text-to-image ratio
• Applies joint SFT with emphasis on image understanding and vision tasks
• Leverages large-scale open-source datasets combined with in-house synthetic vision data, selected for balanced task coverage
• Follows a progressive training strategy: base model → joint mid-training → supervised fine-tuning

🏃 How to run LFM2-VL

You can run LFM2-VL with Hugging Face transformers v4.55 or more recent as follows:

pip install -U transformers pillow

Here is an example of how to generate an answer with transformers in Python:

from transformers import AutoProcessor, AutoModelForImageTextToText
from transformers.image_utils import load_image
# Load model and processor
model_id = "LiquidAI/LFM2-VL-1.6B"
model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype="bfloat16",
    trust_remote_code=True
)
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
# Load image and create conversation
url = "https://www.ilankelman.org/stopsigns/australia.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 depicts a vibrant street scene in what appears to be a Chinatown or similar cultural area. The focal point is a large red stop sign with white lettering, mounted on a pole.

You can directly run and test the model with this Colab notebook.

🔧 How to fine-tune

We recommend fine-tuning LFM2-VL models on your use cases to maximize performance.

📈 Performance

We obtained MM-IFEval and InfoVQA (Val) scores for InternVL 3 and SmolVLM2 models using VLMEvalKit.

📬 Contact

If you are interested in custom solutions with edge deployment, please contact our sales team.