Image-to-Text
Transformers
Safetensors
mistral3
text-generation
ocr
document-understanding
vision-language
pdf
tables
forms
Eval Results
🇪🇺 Region: EU
Instructions to use lightonai/LightOnOCR-1B-1025 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lightonai/LightOnOCR-1B-1025 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="lightonai/LightOnOCR-1B-1025")# Load model directly from transformers import AutoProcessor, AutoModelForSeq2SeqLM processor = AutoProcessor.from_pretrained("lightonai/LightOnOCR-1B-1025") model = AutoModelForSeq2SeqLM.from_pretrained("lightonai/LightOnOCR-1B-1025", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Congrats!
#3
by CyborgPaloma - opened
To be getting so much traction so soon after deepseek's OCR release just goes to show how good this paper is and how useful and needed it still is. Hopefully that is inspiring the confidence in your team that it aught to. Thank you for the apache-2.0!!
Thanks for the kind words!
Yeah, true!
This kind of "playing around" with the tokenizer is such a great idea, all the following matrixes get smaller, leading to such incredible performance gains.