File size: 8,518 Bytes
331cacf 73ae087 331cacf 73ae087 331cacf 0ca690c 331cacf ef6a2da 331cacf ebe9d15 331cacf 2443671 8df806b 89330e2 331cacf |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 |
---
license: apache-2.0
language:
- en
base_model:
- openai/clip-vit-large-patch14-336
- allenai/OLMo-7B-1124
pipeline_tag: image-text-to-text
tags:
- multimodal
- olmo
- molmo
- pixmo
---
<img src="molmo_logo.png" alt="Logo for the Molmo Project" style="width: auto; height: 50px;">
# Molmo 7B-O
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](https://huggingface.co/collections/allenai/molmo-66f379e6fe3b8ef090a8ca19).
**Learn more** about the Molmo family [in our announcement blog post](https://molmo.allenai.org/blog).
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.
It performs comfortably between GPT-4V and GPT-4o on both academic benchmarks and human evaluation.
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**](https://docs.google.com/forms/d/e/1FAIpQLSdML1MhNNBDsCHpgWG65Oydg2SjZzVasyqlP08nBrWjZp_c7A/viewform) to be the first to know when artifacts are released.
Quick links:
- 💬 [Demo](https://molmo.allenai.org/)
- 📂 [All Models](https://huggingface.co/collections/allenai/molmo-66f379e6fe3b8ef090a8ca19)
- 📃 [Paper](https://molmo.allenai.org/paper.pdf)
- 🎥 [Blog with Videos](https://molmo.allenai.org/blog)
## Quick Start
To run Molmo, first install dependencies:
```bash
# uninstall all tensorflow packages
pip list --format=freeze | grep '^tensorflow' | cut -d= -f1 | xargs -n1 pip uninstall -y
# install CPU-only version of tensorflow; used for image preprocessing
pip install einops tensorflow-cpu 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-O-0924',
trust_remote_code=True,
torch_dtype='auto',
device_map='auto'
)
# load the model
model = AutoModelForCausalLM.from_pretrained(
'allenai/Molmo-7B-O-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 ...
```
## Evaluations
| Model | Average Score on 11 Academic Benchmarks | Human Preference Elo Rating |
|-----------------------------|-----------------------------------------|-----------------------------|
| Molmo 72B | 81.2 | 1077 |
| Molmo 7B-D | 77.3 | 1056 |
| **Molmo 7B-O (this model)** | **74.6** | **1051** |
| MolmoE 1B | 68.6 | 1032 |
| GPT-4o | 78.5 | 1079 |
| GPT-4V | 71.1 | 1041 |
| Gemini 1.5 Pro | 78.3 | 1074 |
| Gemini 1.5 Flash | 75.1 | 1054 |
| Claude 3.5 Sonnet | 76.7 | 1069 |
| Claude 3 Opus | 66.4 | 971 |
| Claude 3 Haiku | 65.3 | 999 |
| Qwen VL2 72B | 79.4 | 1037 |
| Qwen VL2 7B | 73.7 | 1025 |
| Intern VL2 LLAMA 76B | 77.1 | 1018 |
| Intern VL2 8B | 69.4 | 953 |
| Pixtral 12B | 69.5 | 1016 |
| Phi3.5-Vision 4B | 59.7 | 982 |
| PaliGemma 3B | 50.0 | 937 |
| LLAVA OneVision 72B | 76.6 | 1051 |
| LLAVA OneVision 7B | 72.0 | 1024 |
| Cambrian-1 34B | 66.8 | 953 |
| Cambrian-1 8B | 63.4 | 952 |
| xGen - MM - Interleave 4B | 59.5 | 979 |
| LLAVA-1.5 13B | 43.9 | 960 |
| LLAVA-1.5 7B | 40.7 | 951 |
*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).*
## FAQs
### 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:
```python
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):
```python
# 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'
)
```
## 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).
|