Complex vision-language tasks like image understanding and question answering typically require large, resource-intensive models that are difficult to deploy and run on regular hardware. This results in high computational costs and limited accessibility for many developers.
With Moondream, you can:
- Run vision-language tasks locally
- Use a lightweight model (500MB-2GB)
- Process images without internet connectivity
- Get fast responses for basic image understanding
Let’s say we have the following image:
We can use Moondreem to ask some questions about the image:
import moondream as md
from PIL import Image
# Load lightweight model locally
model = md.vl(model="path/to/moondream-0.5b-int8.mf") # Only 593MB
# Process image
image = Image.open("path/to/image.jpg")
encoded_image = model.encode_image(image)
# Ask questions about the image
answer1 = model.query(encoded_image, "What is the girl doing?")["answer"]
print(answer1)
# The girl is sitting at a table and eating a large hamburger.
answer2 = model.query(encoded_image, "What color is the girl's hair?")["answer"]
print(answer2)
# The girl's hair is white.This example shows how Moondream makes vision-language tasks accessible by providing a compact model that can run on regular hardware while still maintaining good performance for basic image-understanding tasks.
