Instructions to use hf-internal-testing/tiny-random-ConvBertModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-ConvBertModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-ConvBertModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-ConvBertModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-ConvBertModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 698b6b3a7743315d4a815f28aa4f9feca8b3ba09194f89268301b432a92d7f66
- Size of remote file:
- 5.47 MB
- SHA256:
- 5e90139bb69375108675426b51de6b59c71a3270e9a2f6e983680ce7c4f573c7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.