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
File size: 219 Bytes
1835135 | 1 2 3 4 5 6 7 8 9 10 11 12 13 | {
"bos_token_id": 151643,
"do_sample": true,
"eos_token_id": [
151645,
151643
],
"pad_token_id": 151643,
"temperature": 0.2,
"top_k": 0,
"top_p": 0.9,
"transformers_version": "5.0.0.dev0"
} |