Image-Text-to-Text
MLX
Safetensors
mistral3
ocr
document-understanding
vision-language
pdf
tables
forms
conversational
4-bit precision
Instructions to use humpf/LightOnOCR-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use humpf/LightOnOCR-4bit with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("humpf/LightOnOCR-4bit") config = load_config("humpf/LightOnOCR-4bit") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
| license: apache-2.0 | |
| pipeline_tag: image-text-to-text | |
| language: | |
| - en | |
| - fr | |
| - de | |
| - es | |
| - it | |
| - nl | |
| - pt | |
| - sv | |
| - da | |
| - zh | |
| - ja | |
| library_name: mlx | |
| tags: | |
| - ocr | |
| - document-understanding | |
| - vision-language | |
| - tables | |
| - forms | |
| - mlx | |
| base_model: lightonai/LightOnOCR-2-1B | |