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*This model was contributed to Hugging Face Transformers on 2026-05-05.*

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# Granite4Vision

[Granite Vision 4.1](https://huggingface.co/ibm-granite/granite-vision-4.1-4b) is a vision-language model from IBM Research designed for enterprise-grade document data extraction. It specializes in chart extraction (Chart2CSV, Chart2Summary, Chart2Code), table extraction (JSON, HTML, OTSL), and semantic key-value pair extraction.

The model builds on [LLaVA-NeXT](llava_next) with several architectural innovations:

1. **SigLIP2 Vision Encoder** ([`google/siglip2-so400m-patch16-384`](https://huggingface.co/google/siglip2-so400m-patch16-384)): images are tiled into 384x384 patches.
2. **Window Q-Former Projectors**: compress visual features 4x using windowed cross-attention over 4x4 patch windows into 2x2 tokens.
3. **DeepStack Feature Injection** with 8 vision-to-LLM injection points:
   - *LayerDeepstack*: features from 4 vision encoder depths are projected into different early LLM layers.
   - *SpatialDeepstack*: deepest vision features are split into 4 spatial groups and injected at later LLM layers.
4. **Language Model**: [Granite 4.1](https://huggingface.co/ibm-granite/granite-4.1-4b-base) (4B params) with LoRA adapters (rank 256) across all self-attention and MLP layers.

The model is delivered as a LoRA adapter on top of the base LLM, enabling single deployments to support both multimodal and text-only workloads. Total parameter count is ~4B.

> [!TIP]
> This model was contributed by the [IBM Granite Vision Team](https://github.com/ibm-granite).

## Usage Tips

- Set `padding_side="left"` during batched generation for more accurate results.

```py
processor.tokenizer.padding_side = "left"
```

- The model supports specialized task tags for document extraction: `<chart2csv>`, `<chart2summary>`, `<chart2code>`, `<tables_html>`, `<tables_otsl>`, `<tables_json>`. Pass these as the text prompt along with a document image.

- For key-value pair extraction, provide a JSON schema describing the fields to extract. The model returns structured JSON matching the schema.

The example below demonstrates how to generate text based on an image with [`Pipeline`] or the [`AutoModel`] class.

<hfoptions id="usage">

<hfoption id="Pipeline">

```python
from transformers import pipeline

pipe = pipeline(
    task="image-text-to-text",
    model="ibm-granite/granite-vision-4.1-4b",
)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"},
            {"type": "text", "text": "Describe this image."},
        ],
    }
]
pipe(text=messages, max_new_tokens=100, return_full_text=False)
```

</hfoption>

<hfoption id="AutoModel">

```python
import torch
from transformers import AutoProcessor, AutoModelForImageTextToText

model_id = "ibm-granite/granite-vision-4.1-4b"

processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(model_id, device_map="auto")

conversation = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"},
            {"type": "text", "text": "Describe this image."},
        ],
    },
]
inputs = processor.apply_chat_template(
    conversation,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

output = model.generate(**inputs, max_new_tokens=100)
print(processor.decode(output[0], skip_special_tokens=True))
```

</hfoption>

</hfoptions>

Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the [Quantization](../quantization/overview) overview for more available quantization backends.

The example below uses [bitsandbytes](../quantization/bitsandbytes) to only quantize the weights to int4.

```python
import torch
from transformers import AutoProcessor, AutoModelForImageTextToText, BitsAndBytesConfig

quant_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_quant_type="nf4",
)

model_id = "ibm-granite/granite-vision-4.1-4b"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(
    model_id, quantization_config=quant_config, device_map="auto"
)

conversation = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"},
            {"type": "text", "text": "Describe this image."},
        ],
    },
]
inputs = processor.apply_chat_template(
    conversation,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

output = model.generate(**inputs, max_new_tokens=100)
print(processor.decode(output[0], skip_special_tokens=True))
```

## Granite4VisionConfig

[[autodoc]] Granite4VisionConfig

## Granite4VisionTextConfig

[[autodoc]] Granite4VisionTextConfig

## Granite4VisionProcessor

[[autodoc]] Granite4VisionProcessor
    - __call__

## Granite4VisionModel

[[autodoc]] Granite4VisionModel

## Granite4VisionTextModel

[[autodoc]] Granite4VisionTextModel

## Granite4VisionForConditionalGeneration

[[autodoc]] Granite4VisionForConditionalGeneration
    - forward
    - get_image_features
