Instructions to use hf-tiny-model-private/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-tiny-model-private/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-tiny-model-private/tiny-random-VisionTextDualEncoderModel-vit-bert")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-VisionTextDualEncoderModel-vit-bert") model = AutoModelForMultimodalLM.from_pretrained("hf-tiny-model-private/tiny-random-VisionTextDualEncoderModel-vit-bert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8f9b2f6cfec44289f9ae3703b42c74e8a925483eb730e7a65eec95a14be35d75
- Size of remote file:
- 717 kB
- SHA256:
- 05ca67d87d014d601a2a56a574efc0cf15d8a8c314cce607e095e3b67adf0856
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