Instructions to use fredwang08/bert-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fredwang08/bert-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="fredwang08/bert-test")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("fredwang08/bert-test") model = AutoModel.from_pretrained("fredwang08/bert-test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from fredwang08/bert-test: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/fredwang08/bert-test/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://fredwang08/bert-test/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/fredwang08/bert-test/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 2957f9bfcf6bc0119ff33b656ad1031e73454013610dbd94091babc08f5b7036
- Size of remote file:
- 438 MB
- SHA256:
- 518335c53d4ec0e9ada147c43c2b514000297d674a4d5993a39bb9df4a8a49b4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.