Instructions to use mlx-community/FastVLM-0.5B-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/FastVLM-0.5B-bf16 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/FastVLM-0.5B-bf16") config = load_config("mlx-community/FastVLM-0.5B-bf16") # 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: 788 Bytes
0bc5eb1 | 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 36 37 38 39 | {
"auto_map": {
"AutoImageProcessor": "processing_fastvlm.FastVLMImageProcessor",
"AutoProcessor": "processing_fastvlm.FastVLMProcessor"
},
"crop_size": {
"height": 1024,
"width": 1024
},
"data_format": "channels_first",
"default_to_square": false,
"device": null,
"disable_grouping": null,
"do_center_crop": true,
"do_convert_rgb": true,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.0,
0.0,
0.0
],
"image_processor_type": "FastVLMImageProcessor",
"image_std": [
1.0,
1.0,
1.0
],
"input_data_format": null,
"processor_class": "FastVLMProcessor",
"resample": 3,
"rescale_factor": 0.00392156862745098,
"return_tensors": null,
"size": {
"shortest_edge": 1024
}
}
|