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)# pip install -U transformers accelerate # 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
Download processor_config.json from sujet-ai/Lutece-Vision-Base: direct link, hf CLI and curl.
- Browser
- Download file 136 Bytes
-
https://huggingface.co/sujet-ai/Lutece-Vision-Base/resolve/main/processor_config.json
- Command line
-
hf download hf://sujet-ai/Lutece-Vision-Base/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/sujet-ai/Lutece-Vision-Base/resolve/main/processor_config.json
136 Bytes
| { | |
| "auto_map": { | |
| "AutoProcessor": "processing_florence2.Florence2Processor" | |
| }, | |
| "processor_class": "Florence2Processor" | |
| } | |