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:
- cef8e9ab110b9772c9123732bbab1ad45fc7da289deed031239074605157708c
- Size of remote file:
- 905 kB
- SHA256:
- 01b13120a46e74b2f55b6da800cbd2fee5979afe9da401586f0721bc2d2bbe0c
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