Instructions to use junzai/bert_funting_test_ai10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use junzai/bert_funting_test_ai10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="junzai/bert_funting_test_ai10")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("junzai/bert_funting_test_ai10") model = AutoModelForMaskedLM.from_pretrained("junzai/bert_funting_test_ai10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from junzai/bert_funting_test_ai10: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/junzai/bert_funting_test_ai10/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://junzai/bert_funting_test_ai10/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/junzai/bert_funting_test_ai10/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 68745b98825cc03f8857472328524a9a174b3a89219ae8b06122c638f2f6ff76
- Size of remote file:
- 438 MB
- SHA256:
- 59fc12a028a5cadf969a9b41d00539fbb4f088ed7beaaf94ed9c79b59f99920e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.