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
- Atomic Chat
- Xet hash:
- c5fe479de3940f52337a611079801a2c7c1c34c9945bd63483ae8cae2f8d4497
- Size of remote file:
- 11.4 MB
- SHA256:
- 22a32bc7af1fc17ed370988966140a379f0421b0a508e4ae3fe4bcf7a86644e1
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