README.md

---
library_name: transformers
license: apache-2.0
base_model:
- allenai/Molmo-7B-D-0924
base_model_relation: quantized
tags:
  - bitsandbytes
  - Molmo
  - chat
  - multimodal
---
## 4-bit quantization of the original [Molmo-7B-D-0924](https://huggingface.co/allenai/Molmo-7B-D-0924) model using ```bitsandbytes```.

NOTE: This quantization differs from [the one here](https://huggingface.co/cyan2k/molmo-7B-D-bnb-4bit) by slightly modifying the source code to remove unnecessary dependencies and otherwise make it work out of the box.

<details><summary>Original Model Card</summary>

# Molmo 7B-D

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 7B-D is based on [Qwen2-7B](https://huggingface.co/Qwen/Qwen2-7B) 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.
It powers the **Molmo demo at** [**molmo.allenai.org**](https://molmo.allenai.org).

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

# 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 image features an adorable black Labrador puppy, captured from a top-down
#      perspective. The puppy is sitting on a wooden deck, which is composed ...
```

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:

```python
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                   | 81.2                                    | 1077                        |
| **Molmo 7B-D (this model)** | **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).

</details>

<br>

# Usage:

The example script below requires an Nvidia GPU and that you `pip install` the CUDA libraries into your virtual environment.  Pip installing the CUDA libraries is NOT required if you install them systemwide (as most people do), in which case just remove the simply remove the ```set_cuda_paths``` function.  However, make sure that you've installed compatible CUDA and Pytorch versions.

<details><summary>COMPATIBLE CUDA AND PYTORCH 2.2.2 COMBINATIONS</summary>

Pytorch is only tested with specific versions of CUDA.  When using pytorch 2.2.2, the following CUDA versions are required:

- ```pip install nvidia-cublas-cu12==12.1.3.1```
- ```pip install nvidia-cuda-runtime-cu12==12.1.105```
- ```pip install nvidia-cuda-nvrtc-cu12==12.1.105```
- ```pip install nvidia-cudnn-cu12==8.9.2.26```
-  Then install [`torch==2.2.2`](https://download.pytorch.org/whl/cu121/torch/), [`torchvision==0.17`](https://download.pytorch.org/whl/cu121/torchvision/), and [`torchaudio==2.2.2`](https://download.pytorch.org/whl/cu121/torchaudio/) by visiting each of these three links and creating a `pip install` command based on the link for your Python version and platform.

For example, for Windows using Python 3.11 you would use the following:

```
pip install https://download.pytorch.org/whl/cu121/torch-2.2.2%2Bcu121-cp311-cp311-win_amd64.whl#sha256=efbcfdd4399197d06b32f7c0e1711c615188cdd65427b933648c7478fb880b3f
```
```
pip install https://download.pytorch.org/whl/cu121/torchvision-0.17.2%2Bcu121-cp311-cp311-win_amd64.whl#sha256=10ad542aab6b47dbe73c441381986d50a7ed5021cbe01d593a14477ec1f067a0
```
```
pip install https://download.pytorch.org/whl/cu121/torchaudio-2.2.2%2Bcu121-cp311-cp311-win_amd64.whl#sha256=c7dee68cd3d2b889bab71d4a0c345bdc3ea2fe79a62b921a6b49292c605b6071
```
</details>

<details><summary>COMPATIBLE CUDA AND PYTORCH 2.5.1 COMBINATIONS</summary>

Pytorch is only tested with specific versions of CUDA.  When using pytorch 2.5.1, the following CUDA versions are required:

- ```pip install nvidia-cublas-cu12==12.4.5.8```
- ```pip install nvidia-cuda-runtime-cu12==12.4.127```
- ```pip install nvidia-cuda-nvrtc-cu12==12.4.127```
- ```pip install nvidia-cudnn-cu12==9.1.0.70```
-  Then install [`torch==2.5.1`](https://download.pytorch.org/whl/cu124/torch/), [`torchvision==0.20.1`](https://download.pytorch.org/whl/cu124/torchvision/), and [`torchaudio==2.5.1`](https://download.pytorch.org/whl/cu124/torchaudio/) by visiting each of these three links and creating a `pip install` command based on the link for your Python version and platform.

For example, for Windows using Python 3.11 you would use the following:

```
pip install https://download.pytorch.org/whl/cu124/torch-2.5.1%2Bcu124-cp311-cp311-win_amd64.whl#sha256=6c8a7003ef1327479ede284b6e5ab3527d3900c2b2d401af15bcc50f2245a59f
```
```
pip install https://download.pytorch.org/whl/cu124/torchvision-0.20.1%2Bcu124-cp311-cp311-win_amd64.whl#sha256=15796b453a99ed0f0cbc249d129685ddc88157310135fb3addaf738a15db5306
```
```
pip install https://download.pytorch.org/whl/cu124/torchaudio-2.5.1%2Bcu124-cp311-cp311-win_amd64.whl#sha256=b3d75f4e6efc5412fe78c7f2787ee4f39cea1317652e1a47785879cde109f5c4
```
</details>

<details><summary>COMPATIBLE CUDA AND PYTORCH 2.6.0 COMBINATIONS</summary>

Pytorch is only tested with specific versions of CUDA.  When using pytorch 2.5.1, the following CUDA versions are required:

- ```pip install nvidia-cublas-cu12==12.6.4.1```
- ```pip install nvidia-cuda-runtime-cu12==12.6.77```
- ```pip install nvidia-cuda-nvrtc-cu12==12.6.77```
- ```pip install nvidia-cudnn-cu12==9.5.1.17```
-  Then install [`torch==2.6.0`](https://download.pytorch.org/whl/cu126/torch/), [`torchvision==0.21.0`](https://download.pytorch.org/whl/cu126/torchvision/), and [`torchaudio==2.6.0`](https://download.pytorch.org/whl/cu126/torchaudio/) by visiting each of these three links and creating a `pip install` command based on the link for your Python version and platform.

For example, for Windows using Python 3.11 you would use the following:

```
pip install https://download.pytorch.org/whl/cu126/torch-2.6.0%2Bcu126-cp311-cp311-win_amd64.whl#sha256=5ddca43b81c64df8ce0c59260566e648ee46b2622ab6a718e38dea3c0ca059a1
```
```
pip install https://download.pytorch.org/whl/cu126/torchvision-0.21.0%2Bcu126-cp311-cp311-win_amd64.whl#sha256=ddbf4516fbb7624ac42934b877dcf6a3b295d9914ab89643b55dedb9c9773ce4
```
```
pip install https://download.pytorch.org/whl/cu126/torchaudio-2.6.0%2Bcu126-cp311-cp311-win_amd64.whl#sha256=833b8e350c77021400fed2271df10ecd02b88f684bbc9d57132faa0efc9a0a57
```
</details>


Example script (process single image):

```Python
import sys
import os
from pathlib import Path

def set_cuda_paths():
   venv_base = Path(sys.executable).parent.parent
   nvidia_base_path = venv_base / 'Lib' / 'site-packages' / 'nvidia'
   cuda_path = nvidia_base_path / 'cuda_runtime' / 'bin'
   cublas_path = nvidia_base_path / 'cublas' / 'bin'
   cudnn_path = nvidia_base_path / 'cudnn' / 'bin'
   nvrtc_path = nvidia_base_path / 'cuda_nvrtc' / 'bin'
   
   paths_to_add = [
       str(cuda_path),
       str(cublas_path),
       str(cudnn_path),
       str(nvrtc_path),
   ]
   env_vars = ['CUDA_PATH', 'PATH']
   
   for env_var in env_vars:
       current_value = os.environ.get(env_var, '')
       new_value = os.pathsep.join(paths_to_add + [current_value] if current_value else paths_to_add)
       os.environ[env_var] = new_value

set_cuda_paths()

import torch
from PIL import Image
from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig

model_path = r"[INSERT THE PATH TO THE FOLDER HOLDING THE MODEL FILES HERE]"

class VisionModel:
   def __init__(self):
       self.model = None
       self.processor = None
       self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

   def initialize_model_and_processor(self):
       self.processor = AutoProcessor.from_pretrained(
           model_path,
           trust_remote_code=True,
           torch_dtype='auto',
           device_map='auto'
       )
       self.model = AutoModelForCausalLM.from_pretrained(
           model_path,
           trust_remote_code=True,
           torch_dtype='auto',
           device_map='auto'
       )
       
   def process_single_image(self, image_path):
       image = Image.open(image_path)
       if image.mode != "RGB":
           image = image.convert("RGB")
       text = "Describe this image in detail as possible but be succinct and don't repeat yourself."

       inputs = self.processor.process(images=[image], text=text)
       inputs = {k: v.to(self.device).unsqueeze(0) for k, v in inputs.items()}

       output = self.model.generate_from_batch(
           inputs,
           GenerationConfig(max_new_tokens=500, stop_strings=["<|endoftext|>"]),
           tokenizer=self.processor.tokenizer
       )

       generated_text = self.processor.tokenizer.decode(output[0, inputs['input_ids'].size(1):], skip_special_tokens=True)
       print(f"\nGenerated Text:\n{generated_text}\n")

if __name__ == "__main__":
   image_path = r"[INSERT THE PATH TO THE IMAGE YOU WANT TO PROCESS HERE]"
   vision_model = VisionModel()
   vision_model.initialize_model_and_processor()
   vision_model.process_single_image(image_path)
```