Image-Text-to-Text
MLX
Safetensors
Japanese
English
sarashina2_vision
ocr
vision
japanese
conversational
custom_code
4-bit precision
Instructions to use tokimoa/sarashina2.2-ocr-mlx-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use tokimoa/sarashina2.2-ocr-mlx-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("tokimoa/sarashina2.2-ocr-mlx-4bit") config = load_config("tokimoa/sarashina2.2-ocr-mlx-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 Settings
- LM Studio
File size: 646 Bytes
3d1d12c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | {
"auto_map": {
"AutoProcessor": "processing_sarashina2_vision.Sarashina2VisionProcessor"
},
"do_convert_rgb": true,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.5,
0.5,
0.5
],
"image_processor_type": "Sarashina2VisionImageProcessor",
"image_std": [
0.5,
0.5,
0.5
],
"max_pixels": 2458624,
"merge_size": 2,
"min_pixels": 3136,
"patch_size": 14,
"processor_class": "Sarashina2VisionProcessor",
"resample": 2,
"rescale_factor": 0.00392156862745098,
"size": {
"longest_edge": 2458624,
"shortest_edge": 3136
},
"temporal_patch_size": 2
}
|