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--- |
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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- openai/clip-vit-large-patch14-336 |
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- allenai/OLMo-7B-1124 |
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pipeline_tag: image-text-to-text |
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tags: |
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- multimodal |
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- olmo |
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- molmo |
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- pixmo |
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library_name: transformers |
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--- |
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<img src="molmo_logo.png" alt="Logo for the Molmo Project" style="width: auto; height: 50px;"> |
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# Molmo 7B-O |
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Molmo is a family of open vision-language models developed by the Allen Institute for AI. |
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Molmo models are trained on PixMo, a dataset of 1 million, highly-curated image-text pairs. |
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It has state-of-the-art performance among multimodal models with a similar size while being fully open-source. |
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You can find all models in the Molmo family [here](https://huggingface.co/collections/allenai/molmo-66f379e6fe3b8ef090a8ca19). |
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**Learn more** about the Molmo family [in our announcement blog post](https://molmo.allenai.org/blog) or the [paper](https://huggingface.co/papers/2409.17146). |
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Molmo 7B-O is based on [OLMo-7B-1124]() (to be released) and uses [OpenAI CLIP](https://huggingface.co/openai/clip-vit-large-patch14-336) as vision backbone. |
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It performs comfortably between GPT-4V and GPT-4o on both academic benchmarks and human evaluation. |
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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. |
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[**Sign up here**](https://docs.google.com/forms/d/e/1FAIpQLSdML1MhNNBDsCHpgWG65Oydg2SjZzVasyqlP08nBrWjZp_c7A/viewform) to be the first to know when artifacts are released. |
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Quick links: |
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- 💬 [Demo](https://molmo.allenai.org/) |
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- 📂 [All Models](https://huggingface.co/collections/allenai/molmo-66f379e6fe3b8ef090a8ca19) |
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- 📃 [Paper](https://molmo.allenai.org/paper.pdf) |
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- 🎥 [Blog with Videos](https://molmo.allenai.org/blog) |
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## Quick Start |
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To run Molmo, first install dependencies: |
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```bash |
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pip install einops torchvision |
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``` |
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Then, follow these steps: |
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```python |
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from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig |
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from PIL import Image |
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import requests |
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# load the processor |
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processor = AutoProcessor.from_pretrained( |
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'allenai/Molmo-7B-O-0924', |
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trust_remote_code=True, |
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torch_dtype='auto', |
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device_map='auto' |
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) |
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# load the model |
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model = AutoModelForCausalLM.from_pretrained( |
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'allenai/Molmo-7B-O-0924', |
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trust_remote_code=True, |
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torch_dtype='auto', |
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device_map='auto' |
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) |
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# process the image and text |
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inputs = processor.process( |
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images=[Image.open(requests.get("https://picsum.photos/id/237/536/354", stream=True).raw)], |
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text="Describe this image." |
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) |
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# move inputs to the correct device and make a batch of size 1 |
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inputs = {k: v.to(model.device).unsqueeze(0) for k, v in inputs.items()} |
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# generate output; maximum 200 new tokens; stop generation when <|endoftext|> is generated |
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output = model.generate_from_batch( |
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inputs, |
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GenerationConfig(max_new_tokens=200, stop_strings="<|endoftext|>"), |
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tokenizer=processor.tokenizer |
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) |
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# only get generated tokens; decode them to text |
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generated_tokens = output[0,inputs['input_ids'].size(1):] |
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generated_text = processor.tokenizer.decode(generated_tokens, skip_special_tokens=True) |
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# print the generated text |
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print(generated_text) |
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# >>> This photograph captures an adorable black Labrador puppy sitting on a weathered |
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# wooden deck. The deck's planks, which are a mix of light and dark brown with ... |
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``` |
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To make inference more efficient, run with autocast: |
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```python |
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with torch.autocast(device_type="cuda", enabled=True, dtype=torch.bfloat16): |
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output = model.generate_from_batch( |
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inputs, |
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GenerationConfig(max_new_tokens=200, stop_strings="<|endoftext|>"), |
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tokenizer=processor.tokenizer |
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) |
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``` |
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We did most of our evaluations in this setting (autocast on, but float32 weights) |
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To even further reduce the memory requirements, the model can be run with bfloat16 weights: |
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```python |
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model.to(dtype=torch.bfloat16) |
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inputs["images"] = inputs["images"].to(torch.bfloat16) |
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output = model.generate_from_batch( |
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inputs, |
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GenerationConfig(max_new_tokens=200, stop_strings="<|endoftext|>"), |
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tokenizer=processor.tokenizer |
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) |
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``` |
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Note that this can sometimes change the output of the model compared to running with float32 weights. |
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## Evaluations |
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| Model | Average Score on 11 Academic Benchmarks | Human Preference Elo Rating | |
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|-----------------------------|-----------------------------------------|-----------------------------| |
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| Molmo 72B | 81.2 | 1077 | |
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| Molmo 7B-D | 77.3 | 1056 | |
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| **Molmo 7B-O (this model)** | **74.6** | **1051** | |
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| MolmoE 1B | 68.6 | 1032 | |
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| GPT-4o | 78.5 | 1079 | |
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| GPT-4V | 71.1 | 1041 | |
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| Gemini 1.5 Pro | 78.3 | 1074 | |
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| Gemini 1.5 Flash | 75.1 | 1054 | |
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| Claude 3.5 Sonnet | 76.7 | 1069 | |
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| Claude 3 Opus | 66.4 | 971 | |
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| Claude 3 Haiku | 65.3 | 999 | |
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| Qwen VL2 72B | 79.4 | 1037 | |
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| Qwen VL2 7B | 73.7 | 1025 | |
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| Intern VL2 LLAMA 76B | 77.1 | 1018 | |
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| Intern VL2 8B | 69.4 | 953 | |
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| Pixtral 12B | 69.5 | 1016 | |
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| Phi3.5-Vision 4B | 59.7 | 982 | |
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| PaliGemma 3B | 50.0 | 937 | |
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| LLAVA OneVision 72B | 76.6 | 1051 | |
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| LLAVA OneVision 7B | 72.0 | 1024 | |
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| Cambrian-1 34B | 66.8 | 953 | |
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| Cambrian-1 8B | 63.4 | 952 | |
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| xGen - MM - Interleave 4B | 59.5 | 979 | |
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| LLAVA-1.5 13B | 43.9 | 960 | |
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| LLAVA-1.5 7B | 40.7 | 951 | |
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*Benchmarks: AI2D test, ChartQA test, VQA v2.0 test, DocQA test, InfographicVQA test, TextVQA val, RealWorldQA, MMMU val, MathVista testmini, CountBenchQA, Flickr Count (we collected this new dataset that is significantly harder than CountBenchQA).* |
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## FAQs |
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### I'm getting an error a broadcast error when processing images! |
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Your image might not be in RGB format. You can convert it using the following code snippet: |
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```python |
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from PIL import Image |
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image = Image.open(...) |
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if image.mode != "RGB": |
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image = image.convert("RGB") |
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``` |
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### Molmo doesn't work great with transparent images! |
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We received reports that Molmo models might struggle with transparent images. |
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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): |
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```python |
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# Load the image |
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url = "..." |
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image = Image.open(requests.get(url, stream=True).raw) |
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# Convert the image to grayscale to calculate brightness |
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gray_image = image.convert('L') # Convert to grayscale |
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# Calculate the average brightness |
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stat = ImageStat.Stat(gray_image) |
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average_brightness = stat.mean[0] # Get the average value |
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# Define background color based on brightness (threshold can be adjusted) |
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bg_color = (0, 0, 0) if average_brightness > 127 else (255, 255, 255) |
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# Create a new image with the same size as the original, filled with the background color |
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new_image = Image.new('RGB', image.size, bg_color) |
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# Paste the original image on top of the background (use image as a mask if needed) |
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new_image.paste(image, (0, 0), image if image.mode == 'RGBA' else None) |
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# Now you can pass the new_image to Molmo |
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processor = AutoProcessor.from_pretrained( |
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'allenai/Molmo-7B-D-0924', |
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trust_remote_code=True, |
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torch_dtype='auto', |
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device_map='auto' |
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) |
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``` |
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## License and Use |
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This model is licensed under Apache 2.0. It is intended for research and educational use. |
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For more information, please see our [Responsible Use Guidelines](https://allenai.org/responsible-use). |
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