Instructions to use junzai/222 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use junzai/222 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="junzai/222")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("junzai/222") model = AutoModelForMaskedLM.from_pretrained("junzai/222", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from junzai/222: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/junzai/222/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://junzai/222@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/junzai/222/resolve/refs%2Fpr%2F1/pytorch_model.bin
438 MB
- Xet hash:
- bbd65f8ba0d3615987def0da54216c3d73bc9ab6a917d4e5f90c67d1225522b6
- Size of remote file:
- 438 MB
- SHA256:
- c1122f6b0fad9bba278f2e2035afa37c112c1fcbaa5548a3f27caf1f8b2aa275
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