Instructions to use apple/mobilevit-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use apple/mobilevit-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="apple/mobilevit-small") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("apple/mobilevit-small", dtype="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Add TF weights
Browse filesModel converted by the [`transformers`' `pt_to_tf` CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). All converted model outputs and hidden layers were validated against its Pytorch counterpart.
Maximum crossload output difference=1.287e-04; Maximum crossload hidden layer difference=4.578e-05;
Maximum conversion output difference=1.287e-04; Maximum conversion hidden layer difference=4.578e-05;
CAUTION: The maximum admissible error was manually increased to 0.0002!
- tf_model.h5 +3 -0
tf_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:877957ccd128bf7a15912ce185673262923af6affd9bfea6e7c0713985c83f67
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size 22793944
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