Instructions to use mlx-vision/regnet_y_16gf-mlxim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- mlx-image
How to use mlx-vision/regnet_y_16gf-mlxim with mlx-image:
from mlxim.model import create_model model = create_model(mlx-vision/regnet_y_16gf-mlxim)
- MLX
How to use mlx-vision/regnet_y_16gf-mlxim with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir regnet_y_16gf-mlxim mlx-vision/regnet_y_16gf-mlxim
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
- Xet hash:
- ee10dcafba75ff0653d68adac3b1d15c5eddc6f0148af55bb2d8a845a8add14f
- Size of remote file:
- 335 MB
- SHA256:
- a4535c166b670479a86f81ddc907fdf0dbabbf85a9b3968a0cf89927a6534cc5
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