Image-to-Text
Transformers
Safetensors
falcon_ocr
text-generation
falcon
ocr
vision-language
document-understanding
custom_code
Eval Results
Instructions to use tiiuae/Falcon-OCR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiiuae/Falcon-OCR 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="tiiuae/Falcon-OCR", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("tiiuae/Falcon-OCR", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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by wamreyaz - opened
README.md
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> The first `generate()` call is slower due to `torch.compile` building optimized kernels. Subsequent calls are much faster.
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## Categories
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By default, category is `"plain"` (general text extraction). You can specify a category to use a task-specific prompt:
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}
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```
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## Citation
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> The first `generate()` call is slower due to `torch.compile` building optimized kernels. Subsequent calls are much faster.
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We already use PagedInference which is quite fast for most interactive tasks, but for large-scale deployment, check the Deployment section
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which provides a vLLM backend.
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## Categories
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By default, category is `"plain"` (general text extraction). You can specify a category to use a task-specific prompt:
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}
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```
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## Deployment
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TODO: explain how to set up the vLLM server.
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## Citation
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