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
- LM Studio
Create README.md
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by pcuenq HF Staff - opened
README.md
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---
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license: apple-amlr
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pipeline_tag: image-text-to-text
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tags:
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- mlx
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---
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# mlx-community/FastVLM-0.5B-bf16
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This model was converted to MLX format from [`apple/FastVLM-0.5B`](https://huggingface.co/apple/FastVLM-0.5B) using mlx-vlm from [this PR](https://github.com/Blaizzy/mlx-vlm/pull/502).
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Refer to the [original model card](https://huggingface.co/apple/FastVLM-0.5B) for more details on the model.
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## Use with mlx
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```bash
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pip install -U mlx-vlm
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```
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```bash
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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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```
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