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

Model	Parameters	Description
LFM2.5-1.2B-Base	1.2B	Pre-trained base model for fine-tuning
LFM2.5-1.2B-Instruct	1.2B	General-purpose instruction-tuned model
LFM2.5-1.2B-Thinking	1.2B	General-purpose reasoning model
LFM2.5-1.2B-JP	1.2B	Japanese-optimized chat model
LFM2.5-VL-1.6B	1.6B	Vision-language model with fast inference
LFM2.5-Audio-1.5B	1.5B	Audio-language model for speech and text I/O

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

Model	Description
LFM2.5-VL-1.6B	Original model checkpoint in native format. Best for fine-tuning or inference with Transformers and vLLM.
LFM2.5-VL-1.6B-GGUF	Quantized format for llama.cpp and compatible tools. Optimized for CPU inference and local deployment with reduced memory usage.
LFM2.5-VL-1.6B-ONNX	ONNX Runtime format for cross-platform deployment. Enables hardware-accelerated inference across diverse environments (cloud, edge, mobile).
LFM2.5-VL-1.6B-MLX	MLX format for Apple Silicon. Optimized for fast inference on Mac devices using the MLX framework.

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.

<|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|>

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

python

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 background.

Tool Use

LFM2.5 supports function calling for text only input by applying the chat template with the tokenizer. See the Tool Use documentation for the full guide.

python

tools = [{
    "name": "get_weather",
    "description": "Get current weather for a location",
    "parameters": {
        "type": "object",
        "properties": {"location": {"type": "string"}},
        "required": ["location"]
    }
}]

messages = [{"role": "user", "content": "What's the weather in Paris?"}]

# Apply chat template with tools
inputs = processor.tokenizer.apply_chat_template(
    messages,
    tools=tools,
    add_generation_prompt=True,
    return_tensors="pt",
    return_dict=True,
)
input_ids = inputs["input_ids"].to(model.device)
outputs = model.generate(input_ids, max_new_tokens=256)
response = processor.tokenizer.decode(outputs[0, input_ids.shape[1]:], skip_special_tokens=False)

# <|tool_call_start|>[get_weather(location="Paris")]<|tool_call_end|>I am retrieving the current weather for Paris.<|im_end|>

Name	Description	Docs	Notebook
Transformers	Simple inference with direct access to model internals.	Link	Colab link
vLLM	High-throughput production deployments with GPU.	coming soon	Colab link
llama.cpp	Cross-platform inference with CPU offloading.	Link	Colab link

🔧 Fine-tuning

We recommend fine-tuning LFM2.5-VL-1.6B model on your use cases to maximize performance.

Notebook	Description	Link
SFT (Unsloth)	Supervised Fine-Tuning with LoRA using Unsloth.	Colab link
SFT (TRL)	Supervised Fine-Tuning with LoRA using TRL.	Colab link

📊 Performance

Model	MMStar	MM-IFEval	BLINK	InfoVQA (Val)	OCRBench (v2)	RealWorldQA	MMMU (Val)	MMMB (avg)	Multilingual MMBench (avg)
LFM2.5-VL-1.6B	50.67	52.29	48.82	62.71	41.44	64.84	40.56	76.96	65.90
LFM2-VL-1.6B	49.87	46.35	44.50	58.35	35.11	65.75	39.67	72.13	60.57
InternVL3.5-1B	50.27	36.17	44.19	60.99	33.53	57.12	41.89	68.93	58.32
FastVLM-1.5B	53.13	24.99	43.29	23.92	26.61	61.56	38.78	64.84	50.89

All vision benchmark scores are obtained using VLMEvalKit. Multilingual scores are based on the average of benchmarks translated by GPT-4.1-mini from English to Arabic, Chinese, French, German, Japanese, Korean, and Spanish.

📬 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}
}