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, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# granite-vision-3.3-2b-embedding
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**Model Summary:**
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Granite-vision-3.3-2b-embedding is an efficient embedding model based on granite-vision-3.3-2b. This model is specifically designed for multimodal document retrieval, enabling queries on documents with tables, charts, infographics, and complex
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By removing the need for OCR-based text extractions, granite-vision-3.3-2b-embedding can help simplify and accelerate RAG pipelines.
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**Evaluations:**
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# granite-vision-3.3-2b-embedding
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**Model Summary:**
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Granite-vision-3.3-2b-embedding is an efficient embedding model based on [granite-vision-3.3-2b](https://huggingface.co/ibm-granite/granite-vision-3.3-2b). This model is specifically designed for multimodal document retrieval, enabling queries on documents with tables, charts, infographics, and complex layouts. The model generates ColBERT-style multi-vector representations of pages.
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By removing the need for OCR-based text extractions, granite-vision-3.3-2b-embedding can help simplify and accelerate RAG pipelines.
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**Evaluations:**
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