Instructions to use hf-internal-testing/tiny-random-VisionTextDualEncoderModel-vit-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-VisionTextDualEncoderModel-vit-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-VisionTextDualEncoderModel-vit-bert")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-VisionTextDualEncoderModel-vit-bert") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-VisionTextDualEncoderModel-vit-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 05dc731fc0af027f5135cf77f07eaab59a98860cb9d3541eb340a91c43f9118e
- Size of remote file:
- 905 kB
- SHA256:
- cebcd7bea80fa847e92de0d997b59c2d640aa4793209f5a4676282cbdfdd3cef
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