mlx-community/FastVLM-0.5B-bf16
This model was converted to MLX format from apple/FastVLM-0.5B using mlx-vlm from this PR.
Refer to the original model card for more details on the model.
Use with mlx
pip install -U mlx-vlm
python -m mlx_vlm.generate --model mlx-community/FastVLM-0.5B-bf16 --max-tokens 100 --temperature 0.0 --prompt "Describe this image in detail." --image https://huggingface.co/datasets/huggingface/documentation-images/resolve/0052a70beed5bf71b92610a43a52df6d286cd5f3/diffusers/rabbit.jpg
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Model size
0.6B params
Tensor type
BF16
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Hardware compatibility
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# 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)