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
Download model.safetensors from hf-internal-testing/tiny-random-VisionTextDualEncoderModel-vit-bert: direct link, hf CLI and curl.
- Browser
- Download file 679 kB
-
https://huggingface.co/hf-internal-testing/tiny-random-VisionTextDualEncoderModel-vit-bert/resolve/refs%2Fpr%2F25/model.safetensors
- Command line
-
hf download hf://hf-internal-testing/tiny-random-VisionTextDualEncoderModel-vit-bert@refs/pr/25/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hf-internal-testing/tiny-random-VisionTextDualEncoderModel-vit-bert/resolve/refs%2Fpr%2F25/model.safetensors
679 kB
- Xet hash:
- 4e0e2468277e68de35c2e5ca76f3c050d5ef2671199b3b0cbe430293c46fffc9
- Size of remote file:
- 679 kB
- SHA256:
- be1dd34c36c1b1a4dce7414830bf0aa522acaff21840dce1b7dc59f285807225
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