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
| { | |
| "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 | |
| } | |
| } | |