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*This model was released on 2024-12-18 and added to Hugging Face Transformers on 2025-01-23.*

# Granite Vision

## Overview

The [Granite Vision](https://www.ibm.com/new/announcements/ibm-granite-3-1-powerful-performance-long-context-and-more) model is a variant of [LLaVA-NeXT](llava_next), leveraging a [Granite](granite) language model alongside a [SigLIP](SigLIP) visual encoder. It utilizes multiple concatenated vision hidden states as its image features, similar to [VipLlava](vipllava). It also uses a larger set of image grid pinpoints than the original LlaVa-NeXT models to support additional aspect ratios.

Tips:

- This model is loaded into Transformers as an instance of LlaVA-Next. The usage and tips from [LLaVA-NeXT](llava_next) apply to this model as well.

- You can apply the chat template on the tokenizer / processor in the same way as well. Example chat format:

```bash
"<|user|>\nWhat’s shown in this image?\n<|assistant|>\nThis image shows a red stop sign.<|end_of_text|><|user|>\nDescribe the image in more details.\n<|assistant|>\n"
```

Sample inference:

```python
from transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration
from accelerate import Accelerator

device = Accelerator().device

model_path = "ibm-granite/granite-vision-3.1-2b-preview"
processor = LlavaNextProcessor.from_pretrained(model_path)

model = LlavaNextForConditionalGeneration.from_pretrained(model_path).to(device)

# prepare image and text prompt, using the appropriate prompt template
url = "https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true"

conversation = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": url},
            {"type": "text", "text": "What is shown in this image?"},
        ],
    },
]
inputs = processor.apply_chat_template(
    conversation,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt"
).to(model.device)


# autoregressively complete prompt
output = model.generate(**inputs, max_new_tokens=100)

print(processor.decode(output[0], skip_special_tokens=True))
```

This model was contributed by [Alexander Brooks](https://huggingface.co/abrooks9944).

## LlavaNextConfig

[[autodoc]] LlavaNextConfig

## LlavaNextImageProcessor

[[autodoc]] LlavaNextImageProcessor
    - preprocess

## LlavaNextProcessor

[[autodoc]] LlavaNextProcessor
    - __call__

## LlavaNextForConditionalGeneration

[[autodoc]] LlavaNextForConditionalGeneration
    - forward
