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:
- 999a7301bd13e4934b5405f0a1efb5690aaff981494ca64882e7c27bad5bb564
- Size of remote file:
- 717 kB
- SHA256:
- 238c76067cb43a9f1a109171b0fd683e6a29e613caff75a784563b7a923c8a4c
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