How to use from the
Use from the
MLX library
# Make sure mlx-vlm is installed
# pip install --upgrade mlx-vlm

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load the model
model, processor = load("LiquidAI/LFM2.5-VL-450M-MLX-8bit")
config = load_config("LiquidAI/LFM2.5-VL-450M-MLX-8bit")

# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."

# Apply chat template
formatted_prompt = apply_chat_template(
    processor, config, prompt, num_images=1
)

# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output)

LFM2.5-VL-450M-MLX-8bit

MLX export of LFM2.5-VL-450M for Apple Silicon inference.

LFM2.5-VL-450M is a vision-language model built on the LFM2.5-350M backbone with a SigLIP2 NaFlex vision encoder (86M). It supports OCR, document comprehension, multilingual vision understanding, bounding box prediction, and function calling.

Model Details

Property Value
Parameters 450M
Precision 8-bit
Group Size 64
Size 0.52 GB
Context Length 32K
Vision Encoder SigLIP2 NaFlex (86M)
Native Resolution up to 512x512

Quickstart

uv pip install 'mlx-vlm==0.3.9'
from mlx_vlm import load, generate
from mlx_vlm.utils import load_image

model, processor = load("LiquidAI/LFM2.5-VL-450M-MLX-8bit")

image = load_image("photo.jpg")

# Apply chat template (required for LFM2.5-VL)
messages = [{"role": "user", "content": [
    {"type": "image"},
    {"type": "text", "text": "What do you see in this image?"},
]}]
prompt = processor.apply_chat_template(messages, add_generation_prompt=True)

result = generate(
    model,
    processor,
    prompt,
    [image],
    temp=0.1,
    min_p=0.15,
    repetition_penalty=1.05,
    verbose=True,
)
print(result.text)

Recommended Sampling Parameters

Parameter Value
temperature 0.1
min_p 0.15
repetition_penalty 1.05

License

This model is released under the LFM 1.0 License.

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