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
!
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.