Image-to-Text
Transformers
Safetensors
MLX
mistral3
text-generation
ocr
document-understanding
vision-language
pdf
tables
forms
4-bit precision
Instructions to use mlx-community/LightOnOCR-1B-1025-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mlx-community/LightOnOCR-1B-1025-4bit 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-1025-4bit")# Load model directly from transformers import AutoProcessor, AutoModelForSeq2SeqLM processor = AutoProcessor.from_pretrained("mlx-community/LightOnOCR-1B-1025-4bit") model = AutoModelForSeq2SeqLM.from_pretrained("mlx-community/LightOnOCR-1B-1025-4bit") - MLX
How to use mlx-community/LightOnOCR-1B-1025-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir LightOnOCR-1B-1025-4bit mlx-community/LightOnOCR-1B-1025-4bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
mlx-community/LightOnOCR-1B-1025-4bit
This model was converted to MLX format from lightonai/LightOnOCR-1B-1025 using mlx-vlm version 0.3.5.
Refer to the original model card for more details on the model.
Use with mlx
pip install -U mlx-vlm
python -m mlx_vlm.generate --model mlx-community/LightOnOCR-1B-1025-4bit --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
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Model size
0.5B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
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4-bit