Image-to-Text
Transformers
Safetensors
MLX
mistral3
text-generation
ocr
document-understanding
vision-language
pdf
tables
forms
6-bit
Instructions to use mlx-community/LightOnOCR-1B-1025-6bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mlx-community/LightOnOCR-1B-1025-6bit 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="mlx-community/LightOnOCR-1B-1025-6bit")# Load model directly from transformers import AutoProcessor, AutoModelForSeq2SeqLM processor = AutoProcessor.from_pretrained("mlx-community/LightOnOCR-1B-1025-6bit") model = AutoModelForSeq2SeqLM.from_pretrained("mlx-community/LightOnOCR-1B-1025-6bit") - MLX
How to use mlx-community/LightOnOCR-1B-1025-6bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir LightOnOCR-1B-1025-6bit mlx-community/LightOnOCR-1B-1025-6bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
File size: 703 Bytes
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"processor_class": "LightOnOCRProcessor",
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