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---
license: apache-2.0
language:
- en
base_model:
- openai/clip-vit-large-patch14-336
- Qwen/Qwen2-72B
pipeline_tag: image-text-to-text
tags:
- multimodal
- olmo
- molmo
- pixmo
library_name: transformers
---
<img src="molmo_logo.png" alt="Logo for the Molmo Project" style="width: auto; height: 50px;">
# Molmo 72B
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) or the [paper](https://huggingface.co/papers/2409.17146).
Molmo 72B is based on [Qwen2-72B](https://huggingface.co/Qwen/Qwen2-72B) and uses [OpenAI CLIP](https://huggingface.co/openai/clip-vit-large-patch14-336) as vision backbone.
Molmo-72B achieves the highest academic benchmark score and ranks second on human evaluation, just slightly behind GPT-4o.
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
pip install einops torchvision
```
Then, follow these steps:
```python
from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig
from PIL import Image
import requests
import torch
# load the processor
processor = AutoProcessor.from_pretrained(
'allenai/Molmo-72B-0924',
trust_remote_code=True,
torch_dtype='auto',
device_map='auto'
)
# load the model
model = AutoModelForCausalLM.from_pretrained(
'allenai/Molmo-72B-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 sitting on a wooden deck.
# The puppy is positioned in the center of the frame, looking up at the camera...
```
To make inference more efficient, run with autocast:
```python
with torch.autocast(device_type="cuda", enabled=True, dtype=torch.bfloat16):
output = model.generate_from_batch(
inputs,
GenerationConfig(max_new_tokens=200, stop_strings="<|endoftext|>"),
tokenizer=processor.tokenizer
)
```
We did most of our evaluation in this setting (autocast on, but float32 weights)
To even further reduce the memory requirements, the model can be run with bfloat16 weights:
```
model.to(dtype=torch.bfloat16)
inputs["images"] = inputs["images"].to(torch.bfloat16)
output = model.generate_from_batch(
inputs,
GenerationConfig(max_new_tokens=200, stop_strings="<|endoftext|>"),
tokenizer=processor.tokenizer
)
```
Note that we have observed that this can change the output of the model compared to running with float32 weights.
## Evaluations
| Model | Average Score on 11 Academic Benchmarks | Human Preference Elo Rating |
|-----------------------------|-----------------------------------------|-----------------------------|
| **Molmo 72B (this model)** | **81.2** | **1077** |
| Molmo 7B-D | 77.3 | 1056 |
| Molmo 7B-O | 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).
The base model used is Qwen2-72B, whose license (the Tongyi Qianwen license) you can find [here](https://huggingface.co/Qwen/Qwen2-72B/blob/main/LICENSE). |