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* Update granite vision model path Signed-off-by: Alex-Brooks <Alex.brooks@ibm.com> * Enable granite vision test Signed-off-by: Alex-Brooks <Alex.brooks@ibm.com> --------- Signed-off-by: Alex-Brooks <Alex.brooks@ibm.com>
86 lines
3.0 KiB
Markdown
86 lines
3.0 KiB
Markdown
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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# Granite Vision
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## Overview
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The Granite Vision 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.
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Tips:
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- 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.
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- You can apply the chat template on the tokenizer / processor in the same way as well. Example chat format:
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```bash
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"<|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"
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```
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Sample inference:
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```python
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from transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration
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model_path = "ibm-granite/granite-vision-3.1-2b-preview"
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processor = LlavaNextProcessor.from_pretrained(model_path)
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model = LlavaNextForConditionalGeneration.from_pretrained(model_path).to("cuda")
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# prepare image and text prompt, using the appropriate prompt template
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url = "https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true"
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conversation = [
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{
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"role": "user",
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"content": [
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{"type": "image", "url": url},
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{"type": "text", "text": "What is shown in this image?"},
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],
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},
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]
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inputs = processor.apply_chat_template(
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conversation,
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add_generation_prompt=True,
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tokenize=True,
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return_dict=True,
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return_tensors="pt"
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).to("cuda")
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# autoregressively complete prompt
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output = model.generate(**inputs, max_new_tokens=100)
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print(processor.decode(output[0], skip_special_tokens=True))
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```
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This model was contributed by [Alexander Brooks](https://huggingface.co/abrooks9944).
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## LlavaNextConfig
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[[autodoc]] LlavaNextConfig
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## LlavaNextImageProcessor
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[[autodoc]] LlavaNextImageProcessor
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- preprocess
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## LlavaNextProcessor
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[[autodoc]] LlavaNextProcessor
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## LlavaNextForConditionalGeneration
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[[autodoc]] LlavaNextForConditionalGeneration
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- forward
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