Instructions to use ravilution/MolmoWeb-8B-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use ravilution/MolmoWeb-8B-8bit 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("ravilution/MolmoWeb-8B-8bit") config = load_config("ravilution/MolmoWeb-8B-8bit") # 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: 980 Bytes
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"auto_map": {
"AutoProcessor": "processing_molmo2.Molmo2Processor",
"AutoVideoProcessor": "video_processing_molmo2.Molmo2VideoProcessor"
},
"crop_size": null,
"data_format": "channels_first",
"default_to_square": true,
"device": null,
"do_center_crop": null,
"do_convert_rgb": true,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"do_sample_frames": true,
"fps": null,
"frame_sample_mode": "uniform_last_frame",
"image_mean": [
0.5,
0.5,
0.5
],
"image_std": [
0.5,
0.5,
0.5
],
"input_data_format": null,
"max_fps": 2,
"num_frames": 4,
"pad_size": null,
"patch_size": 14,
"pooling_size": [
3,
3
],
"processor_class": "Molmo2Processor",
"resample": 2,
"rescale_factor": 0.00392156862745098,
"return_metadata": false,
"sampling_fps": 2,
"size": {
"height": 378,
"width": 378
},
"video_metadata": null,
"video_processor_type": "Molmo2VideoProcessor"
}
|