metadata
library_name: mlx
license: other
license_name: lfm1.0
license_link: LICENSE
language:
- ar
- zh
- en
- fr
- de
- hi
- id
- it
- ja
- ko
- pl
- pt
- ru
- es
- th
- vi
pipeline_tag: image-text-to-text
tags:
- liquid
- lfm2
- lfm2-vl
- edge
- lfm2.5
- lfm2.5-vl
- mlx
- mlx_vlm
base_model: LiquidAI/LFM2.5-VL-3B
Try LFM • Documentation • LEAP • Blog
LFM2.5-VL-3B-MLX-5bit
MLX export of LFM2.5-VL-3B for Apple Silicon inference.
LFM2.5-VL-3B is a vision-language model built on the LFM2.5-2.6B backbone with a SigLIP2 NaFlex vision encoder (400M). It supports OCR, document comprehension, multilingual vision understanding, bounding box prediction, and function calling.
Quickstart
uv run --with mlx-vlm mlx_vlm.generate --model LiquidAI/LFM2.5-VL-3B-MLX-5bit --max-tokens 100 --temperature 0.2 --image https://placecats.com/neo/300/200 --prompt "how many animals are in the picture?"
from mlx_vlm import apply_chat_template, generate, load
from mlx_vlm.utils import load_image
model, processor = load("LiquidAI/LFM2.5-VL-3B-MLX-5bit")
image = load_image("https://placecats.com/neo/300/200")
messages = [
{
"role": "user",
"content": [
{"type": "image"},
{"type": "text", "text": "What do you see in this image?"},
],
}
]
prompt = apply_chat_template(
processor,
model.config,
messages,
add_generation_prompt=True,
num_images=1,
)
result = generate(
model,
processor,
prompt,
[image],
temp=0.2,
top_k=50,
repetition_penalty=1.0,
verbose=True,
)
print(result.text)