Image-to-Text
Transformers
PyTorch
English
udop
text-generation
chemistry
markush
cxsmiles
molecular-structure
ocr
document-understanding
vision-language-model
patent-analysis
Instructions to use docling-project/MarkushGrapher-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use docling-project/MarkushGrapher-2 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="docling-project/MarkushGrapher-2")# Load model directly from transformers import AutoProcessor, AutoModelForSeq2SeqLM processor = AutoProcessor.from_pretrained("docling-project/MarkushGrapher-2") model = AutoModelForSeq2SeqLM.from_pretrained("docling-project/MarkushGrapher-2") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
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by SFconvertbot - opened
- model.safetensors +3 -0
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