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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b6ec41bfdd0ddfef6e313ae8f6338e92811920b1fff447363ba2e19025db8446
- Size of remote file:
- 1.12 GB
- SHA256:
- 5e220ea75ff38f16bc2d3d395091a3ba43d8f2f4d7c46190f9d97158dc12f4c1
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