Jooju2872/moondream2

moondream is a small vision language model designed to run efficiently on edge devices. Check out the GitHub repository for details, or try it out on the Hugging Face Space!

This model works on lower Torch version(2.1.1) and adds temperature and top_p parameters.

Benchmarks

Release	VQAv2	GQA	TextVQA	DocVQA	TallyQA (simple/full)	POPE (rand/pop/adv)
2024-08-26 (latest)	80.3	64.3	65.2	70.5	82.6 / 77.6	89.6 / 88.8 / 87.2
2024-07-23	79.4	64.9	60.2	61.9	82.0 / 76.8	91.3 / 89.7 / 86.9
2024-05-20	79.4	63.1	57.2	30.5	82.1 / 76.6	91.5 / 89.6 / 86.2
2024-05-08	79.0	62.7	53.1	30.5	81.6 / 76.1	90.6 / 88.3 / 85.0
2024-04-02	77.7	61.7	49.7	24.3	80.1 / 74.2	-
2024-03-13	76.8	60.6	46.4	22.2	79.6 / 73.3	-
2024-03-06	75.4	59.8	43.1	20.9	79.5 / 73.2	-
2024-03-04	74.2	58.5	36.4	-	-	-

Usage

pip install transformers einops

from transformers import AutoModelForCausalLM, AutoTokenizer
from PIL import Image

model_id = "vikhyatk/moondream2"
revision = "2024-08-26"
model = AutoModelForCausalLM.from_pretrained(
    model_id, trust_remote_code=True, revision=revision
)
tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision)

image = Image.open('<IMAGE_PATH>')
enc_image = model.encode_image(image)
print(model.answer_question(enc_image, "Describe this image.", tokenizer))

The model is updated regularly, so we recommend pinning the model version to a specific release as shown above.