--- 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 --- Logo for the Molmo Project # Molmo 7B-D ## Quick Start To run Molmo, first install dependencies: ```bash pip install einops tensorflow torchvision ``` Then, follow these steps: ```python 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](https://allenai.org/responsible-use).