Instructions to use entfane/llama-guard-binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use entfane/llama-guard-binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="entfane/llama-guard-binary")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("entfane/llama-guard-binary") model = AutoModelForSequenceClassification.from_pretrained("entfane/llama-guard-binary") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 75860198167e31083997d62d5a31c6a0a3f01160926bd500c8aca764579880e3
- Size of remote file:
- 16 MB
- SHA256:
- 7fa35d64acf61c7e0e64846d480e102e3943f5b7cbde0b7603bb17db8c65511f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.