transformers/docs/source/en/model_doc/granitevision.md

Granite Vision

Overview

The Granite Vision model is a variant of LLaVA-NeXT, leveraging a Granite language model alongside a SigLIP visual encoder. It utilizes multiple concatenated vision hidden states as its image features, similar to 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 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:

"<|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:

from transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration

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

model = LlavaNextForConditionalGeneration.from_pretrained(model_path).to("cuda")

# 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("cuda")


# 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.

LlavaNextConfig

autodoc LlavaNextConfig

LlavaNextImageProcessor

autodoc LlavaNextImageProcessor - preprocess

LlavaNextProcessor

autodoc LlavaNextProcessor

LlavaNextForConditionalGeneration

autodoc LlavaNextForConditionalGeneration - forward