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:
- e404651d1899097f4e1f2409434ba991970863ad8217074b67add9096ad92b14
- Size of remote file:
- 905 kB
- SHA256:
- 1693a7091d142c8b562c5bbee28ae2a44db1af0458dd1e8aa89a5d4cfe6010f0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.