Text Classification
Transformers
Safetensors
English
Polish
qwen3_5_text
text-generation
nvfp4
fp4
compressed-tensors
vllm
quantized
tentaguard
guard
security
prompt-injection
tentaflow
Instructions to use TentaFlow/TentaGuard-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TentaFlow/TentaGuard-NVFP4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TentaFlow/TentaGuard-NVFP4")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TentaFlow/TentaGuard-NVFP4") model = AutoModelForCausalLM.from_pretrained("TentaFlow/TentaGuard-NVFP4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 723a0467f56ee4d0cf4d9b621e79eaa1250695550e1fa2b284c4463f4cfcc237
- Size of remote file:
- 789 MB
- SHA256:
- 32dbab2d3db04861d62114b76cb684b1183fbad98ee821f38628cb23c7acd40e
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