Image-to-Text
Transformers
Safetensors
English
florence2
image-text-to-text
finance
vlm
florence
docvqa
visual question answering
custom_code
Instructions to use sujet-ai/Lutece-Vision-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sujet-ai/Lutece-Vision-Base with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="sujet-ai/Lutece-Vision-Base", trust_remote_code=True)# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("sujet-ai/Lutece-Vision-Base", trust_remote_code=True) model = AutoModelForMultimodalLM.from_pretrained("sujet-ai/Lutece-Vision-Base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# Lutece-Vision-Base
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## Model Description
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Lutece-Vision-Base, named after the ancient name of Paris, is a specialized Vision-Language Model (VLM) designed for financial document analysis and question answering. This model is a fine-tuned version of the Microsoft Florence-2-base-ft, specifically tailored to interpret and answer questions about financial documents, reports, and images.
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## Model Description
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Lutece-Vision-Base, named after the ancient name of Paris, is a specialized Vision-Language Model (VLM) designed for financial document analysis and question answering. This model is a fine-tuned version of the Microsoft Florence-2-base-ft, specifically tailored to interpret and answer questions about financial documents, reports, and images.
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