Instructions to use PlusMinus1/omniparser-icon-caption-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use PlusMinus1/omniparser-icon-caption-mlx 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("PlusMinus1/omniparser-icon-caption-mlx") config = load_config("PlusMinus1/omniparser-icon-caption-mlx") # 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: 752 Bytes
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"_valid_processor_keys": [
"images",
"do_resize",
"size",
"resample",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"return_tensors",
"data_format",
"input_data_format",
"do_convert_rgb"
],
"do_convert_rgb": null,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"do_center_crop": false,
"image_processor_type": "CLIPImageProcessor",
"image_seq_length": 577,
"image_mean": [
0.485,
0.456,
0.406
],
"image_std": [
0.229,
0.224,
0.225
],
"processor_class": "Florence2Processor",
"resample": 3,
"size": {
"height": 768,
"width": 768
},
"crop_size": {
"height": 768,
"width": 768
}
} |