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+*This model was contributed to Hugging Face Transformers on 2026-05-05.*
+
+<div style="float: right;">
+  <div class="flex flex-wrap space-x-1">
+        <img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
+        <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+  </div>
+</div>
+
+# 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