Image-Text-to-Text
MLX
Safetensors
English
molmo_point
multimodal
olmo
molmo
molmo2
conversational
custom_code
6-bit
Instructions to use mlx-community/MolmoPoint-8B-6bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/MolmoPoint-8B-6bit 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/MolmoPoint-8B-6bit") config = load_config("mlx-community/MolmoPoint-8B-6bit") # 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: 556 Bytes
52b2706 | 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 34 35 | {
"auto_map": {
"AutoImageProcessor": "image_processing_molmo2.Molmo2ImageProcessor",
"AutoProcessor": "processing_molmo2.Molmo2Processor"
},
"do_convert_rgb": true,
"image_mean": [
0.5,
0.5,
0.5
],
"image_processor_type": "Molmo2ImageProcessor",
"image_std": [
0.5,
0.5,
0.5
],
"max_crops": 24,
"overlap_margins": [
4,
4
],
"patch_size": 14,
"pooling_size": [
2,
2
],
"processor_class": "Molmo2Processor",
"resample": 2,
"size": {
"height": 378,
"width": 378
}
}
|