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
Upload colgranitevision_config.py
Browse files- colgranitevision_config.py +12 -0
colgranitevision_config.py
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from transformers import LlavaNextConfig
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class ColGraniteVisionConfig(LlavaNextConfig):
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model_type = "colgranitevision"
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def __init__(self, **kwargs):
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self.base_model = kwargs.get("base_model", None)
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self.emb_dim_query = kwargs.get("emb_dim_query", 128)
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self.emb_dim_doc = kwargs.get("emb_dim_doc", 128)
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self.adapter_path = kwargs.get("adapter_path", None)
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super().__init__(**kwargs)
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