LFM2.5‑VL-450M is Liquid AI's refreshed version of the first vision-language model, LFM2-VL-450M, built on an updated backbone LFM2.5-350M 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, Portuguese and Spanish.
Bounding box prediction and object detection for grounded visual understanding.
Function calling support for text-only input.
🎥⚡️ You can try LFM2.5-VL-450M running locally in your browser with our real-time video stream captioning WebGPU demo 🎥⚡️
Alternatively, try the API model on the Playground.
MLX format for Apple Silicon. Optimized for fast on-device inference on Mac with mlx-vlm. Also available in 4bit, 5bit, 6bit, and bf16.
We recommend using it for general vision-language workloads, captioning and object detection. It’s not well-suited for knowledge-intensive tasks or fine-grained OCR.
<|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 run LFM2.5-VL-450M 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-450M"
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 captures the iconic Statue of Liberty standing majestically on Liberty Island in New York City. The statue, a symbol of freedom and democracy, is prominently featured in the foreground, its greenish-gray hue contrasting beautifully with the surrounding water.
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.
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|>
LFM2.5-VL-450M improves over LFM2-VL-450M across both vision and language benchmarks, while also adding two new capabilities: bounding box prediction on RefCOCO-M and function calling support measured by BFCLv4.
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, Portuguese, and Spanish.
# custom terms: read the license before you download$ fw get LiquidAI/LFM2.5-VL-450M
# manifest sha256 b8263bc0aca8… · 902 MB · 10 files$ fw verify ./LFM2.5-VL-450M LiquidAI/LFM2.5-VL-450M
Terms that come with these files. LFM 1.0: commercial use only for organisations under US$10M annual revenue. Keep a copy of the license with the files.
Custom license (custom).Read the license text first. Open this panel to show the commands once you accept its terms.
fw.pyChecks the signature and every sha256, tries each source in order, resumes.
