Image-Text-to-Text
Safetensors
MLX
English
mlx-vlm
locateanything
vision
object-detection
grounding
nvidia
eagle
conversational
4-bit precision
Instructions to use mlx-community/LocateAnything-3B-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/LocateAnything-3B-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("mlx-community/LocateAnything-3B-4bit") config = load_config("mlx-community/LocateAnything-3B-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: 480 Bytes
e4517cd | 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 | {
"auto_map": {
"AutoImageProcessor": "image_processing_locateanything.LocateAnythingImageProcessor",
"AutoProcessor": "processing_locateanything.LocateAnythingProcessor"
},
"image_mean": [
0.5,
0.5,
0.5
],
"image_processor_type": "LocateAnythingImageProcessor",
"image_std": [
0.5,
0.5,
0.5
],
"in_token_limit": 25600,
"merge_kernel_size": [
2,
2
],
"patch_size": 14,
"processor_class": "LocateAnythingProcessor"
}
|