granite vision 3.3 2b

---
tags:
- image-to-text
license: apache-2.0
new_version: ibm-granite/granite-4.0-3b-vision
---

granite-vision-3.3-2b

Model Summary: Granite-vision-3.3-2b is a compact and efficient vision-language model, specifically designed for visual document understanding, enabling automated content extraction from tables, charts, infographics, plots, diagrams, and more. Granite-vision-3.3-2b introduces several novel experimental features such as *image segmentation*, *doctags generation*, and *multi-page support* (see Experimental Capabilities for more details) and offers enhanced safety when compared to earlier Granite vision models. The model was trained on a meticulously curated instruction-following data, comprising diverse public and synthetic datasets tailored to support a wide range of document understanding and general image tasks. Granite-vision-3.3-2b was trained by fine-tuning a Granite large language model with both image and text modalities.

Evaluations: We compare the performance of granite-vision-3.3-2b with previous versions of granite-vision models. Evaluations were done using the standard llms-eval benchmark and spanned multiple public benchmarks, with particular emphasis on document understanding tasks while also including general visual question-answering benchmarks.

| | Granite-vision-3.1-2b-preview | Granite-vision-3.2-2b | Granite-vision-3.3-2b |
|-----------|-----------|--------------|----------------|
| Document benchmarks |
| ChartQA | 0.86 | 0.87 | 0.87 |
| DocVQA | 0.88 | 0.89 | 0.91 |
| TextVQA | 0.76 | 0.78 | 0.80 |
| AI2D | 0.78 | 0.76 | 0.77 |
| InfoVQA | 0.63 | 0.64 | 0.68 |
| OCRBench | 0.75 | 0.77 | 0.79 |
| LiveXiv VQA v2 | 0.61 | 0.61 | 0.61 |
| LiveXiv TQA v2 | 0.55 | 0.57 | 0.52 |
| Other benchmarks |
| MMMU | 0.35 | 0.37 | 0.37 |
| VQAv2 | 0.81 | 0.78 | 0.79 |
| RealWorldQA | 0.65 | 0.63 | 0.63 |
| VizWiz VQA | 0.64 | 0.63 | 0.62 |
| OK VQA | 0.57 | 0.56 | 0.55|

• Paper: Granite Vision: a lightweight, open-source multimodal model for enterprise Intelligence. Note that the paper describes Granite Vision 3.2. Granite Vision 3.3 shares most of the technical underpinnings with Granite 3.2. However, there are several enhancements in terms of new and improved vision encoder, many new high quality datasets for training, and several new experimental capabilities.

• Release Date: Jun 11th, 2025

• License: Apache 2.0

Supported Input Format: Currently the model supports English instructions and images (png, jpeg) as input format.

Intended Use: The model is intended to be used in enterprise applications that involve processing visual and text data. In particular, the model is well-suited for a range of visual document understanding tasks, such as analyzing tables and charts, performing optical character recognition (OCR), and answering questions based on document content. Additionally, its capabilities extend to general image understanding, enabling it to be applied to a broader range of business applications. For tasks that exclusively involve text-based input, we suggest using our Granite large language models, which are optimized for text-only processing and offer superior performance compared to this model.

Generation:

Granite Vision model is supported natively transformers>=4.49. Below is a simple example of how to use the granite-vision-3.3-2b model.

Usage with transformers

First, make sure to build the latest versions of transformers:

pip install transformers>=4.49

Then run the code:

from transformers import AutoProcessor, AutoModelForVision2Seq
from huggingface_hub import hf_hub_download
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"
model_path = "ibm-granite/granite-vision-3.3-2b"
processor = AutoProcessor.from_pretrained(model_path)
model = AutoModelForVision2Seq.from_pretrained(model_path).to(device)
prepare image and text prompt, using the appropriate prompt template
img_path = hf_hub_download(repo_id=model_path, filename='example.png')
conversation = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": img_path},
            {"type": "text", "text": "What is the highest scoring model on ChartQA and what is its score?"},
        ],
    },
]
inputs = processor.apply_chat_template(
    conversation,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt"
).to(device)
autoregressively complete prompt
output = model.generate(inputs, max_new_tokens=100)
print(processor.decode(output[0], skip_special_tokens=True))

pip install torch torchvision torchaudio
pip install vllm==0.6.6

from vllm import LLM, SamplingParams
from vllm.assets.image import ImageAsset
from huggingface_hub import hf_hub_download
from PIL import Image
model_path = "ibm-granite/granite-vision-3.3-2b"
model = LLM(
    model=model_path,
)
sampling_params = SamplingParams(
    temperature=0.2,
    max_tokens=64,
)
Define the question we want to answer and format the prompt
image_token = "<image>"
system_prompt = "<|system|>\nA chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.\n"
question = "What is the highest scoring model on ChartQA and what is its score?"
prompt = f"{system_prompt}<|user|>\n{image_token}\n{question}\n<|assistant|>\n"
img_path = hf_hub_download(repo_id=model_path, filename='example.png')
image = Image.open(img_path).convert("RGB")
print(image)
Build the inputs to vLLM; the image is passed as multi_modal_data.
inputs = {
    "prompt": prompt,
    "multi_modal_data": {
        "image": image,
    }
}
outputs = model.generate(inputs, sampling_params=sampling_params)
print(f"Generated text: {outputs[0].outputs[0].text}")

RTVLM Safety Score - [0,10] - Higher is Better

VLGuard Safety Score - [0,10] - Higher is Better**

| | Unsafe Images (Unsafe) | Safe Images with Unsafe Instructions |
|-----------|-----------|--------------|
|Granite-vision-3.1-2b-preview|6.6|8.4|
|Granite-vision-3.2-2b|7.6|8.9|
|Granite-vision-3.3-2b|8.4|9.3|

Experimental Capabilities

Granite-vision-3.3-2b introduces three new experimental capabilities:

(1) Image segmentation: A notebook showing a segmentation example

(2) Doctags generation: Parse document images to structured text in doctags format. Please see Docling project for more details on doctags.

(3) Multipage support: The model was trained to handle question answering (QA) tasks using multiple consecutive pages from a document—up to 8 pages—given the demands of long-context processing. To support such long sequences without exceeding GPU memory limits, we recommend resizing images so that their longer dimension is 768 pixels.

Fine-tuning

For an example of fine-tuning granite-vision-3.3-2b