metadata

license: apache-2.0
language:
  - en
base_model:
  - openai/clip-vit-large-patch14-336
  - Qwen/Qwen2-7B
pipeline_tag: image-text-to-text
tags:
  - multimodal

Molmo 7B-D

Molmo is an open vision-language model 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.

Molmo 7B-D is based on Qwen2-7B and uses OpenAI CLIP as vision backbone. It is trained on our high-quality PixMo dataset.

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.

Quick Start

To run Molmo, first install dependencies:

pip install einops tensorflow torchvision

Then, follow these steps:

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 photograph captures an adorable black Labrador puppy sitting on a weathered
#     wooden deck. The deck's planks, which are a mix of light and dark brown with ...

License and Use

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.