Instructions to use hf-internal-testing/tiny-random-Data2VecVisionModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Data2VecVisionModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="hf-internal-testing/tiny-random-Data2VecVisionModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-Data2VecVisionModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-Data2VecVisionModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 971313769167a7f74203a4bb67ae6b88dd2607c47702cf0bedeac39fb3883e3a
- Size of remote file:
- 208 kB
- SHA256:
- c596f1c5b2614e89cf954b94393d34fc9fe0a77498f96e6b82f5f3d708c6169e
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