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
Upload README.md with huggingface_hub
Browse files
README.md
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---
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license: apache-2.0
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base_model:
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- Qwen/Qwen3.5-0.8B
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pipeline_tag: text-classification
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library_name: transformers
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language:
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- en
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- pl
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tags:
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- nvfp4
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- fp4
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- compressed-tensors
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- vllm
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- quantized
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- tentaguard
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- guard
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- security
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- prompt-injection
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- tentaflow
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---
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# TentaGuard — NVFP4 (W4A4, vLLM)
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**TentaGuard** to lekki klasyfikator bezpieczenstwa (security guard) — fine-tune
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[`Qwen/Qwen3.5-0.8B`](https://huggingface.co/Qwen/Qwen3.5-0.8B). Wykorzystywany **glownie w aplikacji
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[TentaFlow](https://github.com/Slyb00ts/TentaFlow)** do skanowania tresci z zewnatrz — wiadomosci, dokumentow, wynikow
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wyszukiwan w internecie itp. — pod katem **ukrytych atakow** (prompt injection / jailbreak),
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zanim trafia do glownego LLM.
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Model NIE generuje odpowiedzi dla uzytkownika — zwraca pojedyncza cyfre:
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| Etykieta | Znaczenie |
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|----------|-----------|
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| `0` | bezpieczne (benign) |
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| `1` | prompt injection / naduzycie narzedzi (atak techniczny) |
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| `2` | jailbreak (manipulacja behawioralna) |
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Jesli tekst zawiera JEDNOCZESNIE injection i jailbreak → `1`.
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## Format wejscia
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System prompt (klasyfikator) + wiadomosc uzytkownika `<|guard|>\n{tekst}`. **Buduj prompt
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tokenizerem modelu (`apply_chat_template`)** — nie polegaj na generycznym szablonie czatu.
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## Skutecznosc (zbior testowy guard, 58 przykladow)
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- Exact (0/1/2): **~96.6%** (pelna precyzja) / **~94.8%** (Q5_K_M)
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- Safe/Unsafe: **~98.3%**
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## Autorzy
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Model wytrenowany przez: **Katarzyna Nowak**, **Piotr Jarocki**, **Damian Pala**, **Jakub Rurański**.
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## Licencja i atrybucja
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Apache-2.0, dziedziczona z modelu bazowego [`Qwen/Qwen3.5-0.8B`](https://huggingface.co/Qwen/Qwen3.5-0.8B).
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Ten checkpoint to fine-tune do detekcji atakow na potrzeby aplikacji [TentaFlow](https://github.com/Slyb00ts/TentaFlow).
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## Uzycie (vLLM)
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Format `compressed-tensors` (`nvfp4-pack-quantized`): wagi 4-bit (FP4 E2M1, grupy po 16, skale
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FP8 E4M3 + globalna FP32), aktywacje 4-bit (W4A4), `lm_head` w pelnej precyzji. Kalibracja PTQ
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[`llm-compressor`](https://github.com/vllm-project/llm-compressor) na realnych promptach guard.
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NVFP4 jest akcelerowany sprzetowo na **Blackwellu (sm_100+)**; na starszych GPU vLLM laduje go
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jako **weight-only** (mniejszy VRAM, bez akceleracji FP4).
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```bash
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vllm serve TentaFlow/TentaGuard-NVFP4
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```
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