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
Update README.md
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README.md
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@@ -43,7 +43,7 @@ If the text contains BOTH injection and jailbreak → `1`.
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A classifier system prompt + a user message `<|guard|>\n{text}`. **Build the prompt with the
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model tokenizer (`apply_chat_template`)** — do not rely on a generic chat template.
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## Accuracy (guard test set
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- Exact (0/1/2): **~96.6%** (full precision) / **~94.8%** (Q5_K_M)
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- Safe / Unsafe: **~98.3%**
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A classifier system prompt + a user message `<|guard|>\n{text}`. **Build the prompt with the
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model tokenizer (`apply_chat_template`)** — do not rely on a generic chat template.
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## Accuracy (guard test set)
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- Exact (0/1/2): **~96.6%** (full precision) / **~94.8%** (Q5_K_M)
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- Safe / Unsafe: **~98.3%**
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