Instructions to use ibm-granite/granite-vision-3.3-2b-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ibm-granite/granite-vision-3.3-2b-embedding with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ibm-granite/granite-vision-3.3-2b-embedding", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ibm-granite/granite-vision-3.3-2b-embedding", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update modeling_colgranitevision.py
Browse files
modeling_colgranitevision.py
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@@ -7,7 +7,7 @@ from transformers import LlavaNextPreTrainedModel
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from transformers.models.llava_next.modeling_llava_next import LlavaNextForConditionalGeneration
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from transformers.models.llava_next.modeling_llava_next import unpad_image, get_anyres_image_grid_shape
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from colgranitevision_config import ColGraniteVisionConfig
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class LlavaNextWithCustomPacking(LlavaNextForConditionalGeneration):
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from transformers.models.llava_next.modeling_llava_next import LlavaNextForConditionalGeneration
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from transformers.models.llava_next.modeling_llava_next import unpad_image, get_anyres_image_grid_shape
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from .colgranitevision_config import ColGraniteVisionConfig
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class LlavaNextWithCustomPacking(LlavaNextForConditionalGeneration):
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