Molmo is a family of open vision-language models developed by the Allen Institute for AI. Molmo models are trained on PixMo, a dataset of 1 million, highly-curated image-text pairs. It has state-of-the-art performance among multimodal models with a similar size while being fully open-source. You can find all models in the Molmo family here. Learn more about the Molmo family in our announcement blog post or the paper.
Molmo 7B-D is based on Qwen2-7B and uses OpenAI CLIP as vision backbone. It performs comfortably between GPT-4V and GPT-4o on both academic benchmarks and human evaluation. It powers the Molmo demo atmolmo.allenai.org.
This checkpoint is a preview of the Molmo release. All artifacts used in creating Molmo (PixMo dataset, training code, evaluations, intermediate checkpoints) will be made available at a later date, furthering our commitment to open-source AI development and reproducibility.
Sign up here to be the first to know when artifacts are released.
from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig
from PIL import Image
import requests
# load the processor
processor = AutoProcessor.from_pretrained(
'allenai/Molmo-7B-D-0924',
trust_remote_code=True,
torch_dtype='auto',
device_map='auto'
)
# load the model
model = AutoModelForCausalLM.from_pretrained(
'allenai/Molmo-7B-D-0924',
trust_remote_code=True,
torch_dtype='auto',
device_map='auto'
)
# process the image and text
inputs = processor.process(
images=[Image.open(requests.get("https://picsum.photos/id/237/536/354", stream=True).raw)],
text="Describe this image."
)
# move inputs to the correct device and make a batch of size 1
inputs = {k: v.to(model.device).unsqueeze(0) for k, v in inputs.items()}
# generate output; maximum 200 new tokens; stop generation when <|endoftext|> is generated
output = model.generate_from_batch(
inputs,
GenerationConfig(max_new_tokens=200, stop_strings="<|endoftext|>"),
tokenizer=processor.tokenizer
)
# only get generated tokens; decode them to text
generated_tokens = output[0,inputs['input_ids'].size(1):]
generated_text = processor.tokenizer.decode(generated_tokens, skip_special_tokens=True)
# print the generated text
print(generated_text)
# >>> This image features an adorable black Labrador puppy, captured from a top-down
# perspective. The puppy is sitting on a wooden deck, which is composed ...
To make inference more efficient, run with autocast:
I'm getting an error a broadcast error when processing images!#
Your image might not be in RGB format. You can convert it using the following code snippet:
from PIL import Image
image = Image.open(...)
if image.mode != "RGB":
image = image.convert("RGB")
Molmo doesn't work great with transparent images!#
We received reports that Molmo models might struggle with transparent images. For the time being, we recommend adding a white or dark background to your images before passing them to the model. The code snippet below shows how to do this using the Python Imaging Library (PIL):
# Load the image
url = "..."
image = Image.open(requests.get(url, stream=True).raw)
# Convert the image to grayscale to calculate brightness
gray_image = image.convert('L') # Convert to grayscale
# Calculate the average brightness
stat = ImageStat.Stat(gray_image)
average_brightness = stat.mean[0] # Get the average value
# Define background color based on brightness (threshold can be adjusted)
bg_color = (0, 0, 0) if average_brightness > 127 else (255, 255, 255)
# Create a new image with the same size as the original, filled with the background color
new_image = Image.new('RGB', image.size, bg_color)
# Paste the original image on top of the background (use image as a mask if needed)
new_image.paste(image, (0, 0), image if image.mode == 'RGBA' else None)
# Now you can pass the new_image to Molmo
processor = AutoProcessor.from_pretrained(
'allenai/Molmo-7B-D-0924',
trust_remote_code=True,
torch_dtype='auto',
device_map='auto'
)
This model is licensed under Apache 2.0. It is intended for research and educational use. For more information, please see our Responsible Use Guidelines.
Files
25 files · 32.1 GBWeights 32.1 GB (safetensors). Sizes and sha256 from the signed manifest.
allenai <huggingface.co/allenai>Newsgroups: alt.freeweights.olmoSubject: 
