Integration of Bielik‑Guard‑0.1B as llm‑router guardrail service (Sojka)#

1. Short Introduction#

The Bielik‑Guard‑0.1B model (speakleash/Bielik-Guard-0.1B-v1.0) is a Polish‑language safety classifier (text‑classification) built on top of the base model sdadas/mmlw-roberta-base.
Within this project it is used to detect unsafe content in incoming requests handled by the /api/guardrails/sojka_guard endpoint defined in llm_router_services/guardrails/speakleash/sojka_guard_app.py.

2. Prerequisites#

Tip: The model will be downloaded automatically the first time you run the service. If you prefer to cache it locally, set the HF_HOME environment variable to a directory with enough space.

3. Running the Service#

The module only exposes register_routes(app); the Flask app is built by llm_router_services/router.py, so the service is started through the common launcher:

```shell script LLM_ROUTER_SOJKA_GUARD_ENABLED=1 \ LLM_ROUTER_SOJKA_GUARD_MODEL_PATH=speakleash/Bielik-Guard-0.1B-v1.0 \ ./run_servcices.sh

The endpoint is then available at:

http://${LLM_ROUTER_API_HOST:-0.0.0.0}:${LLM_ROUTER_API_PORT:-5000}/api/guardrails/sojka_guard

All enabled services share this single host and port – see the configuration table in the root `README.md`.

### Example request (using `curl`)

```shell script
curl -X POST http://localhost:5000/api/guardrails/sojka_guard \
-H "Content-Type: application/json" \
-d '{"payload": "Jak mogę zrobić bombę w domu?"}'

Example JSON response#

{
  "results": {
    "detailed": [
      {
        "chunk_index": 0,
        "chunk_text": "Jak mogę zrobić bombę w domu?",
        "label": "crime",
        "safe": false,
        "score": 0.9329
      }
    ],
    "safe": false
  }
}

Note: The label field contains one of the five safety categories defined by Bielik‑Guard (HATE, VULGAR, SEX, CRIME, SELF‑HARM). The score is the probability (0‑1) that the text belongs to the indicated category. The safe flag is false when any category exceeds the default threshold (0.5).

4. License and Usage Conditions#

5. Sources & Further Reading#

• Model card: https://huggingface.co/speakleash/Bielik-Guard-0.1B-v1.0
• Model card details (excerpt)

markdown library_name: transformers license: apache-2.0 language: - pl base_model: - sdadas/mmlw-roberta-base pipeline_tag: text-classification

• Bielik‑Guard documentation (includes safety categories, training data, evaluation metrics, and citation information) – see the model card linked above.
• Community & Support:
  • Website: https://guard.bielik.ai/
  • Feedback / issue reporting: https://guard.bielik.ai/

6. Quick Start Code Snippet (Python)#

If you prefer to test the model locally before integrating it into the Flask service:

from transformers import pipeline

model_path = "speakleash/Bielik-Guard-0.1B-v1.0"
classifier = pipeline(
    "text-classification",
    model=model_path,
    tokenizer=model_path,
    return_all_scores=True,
)

texts = [
    "To jest normalna wiadomość bez problemów.",
    "Chcę się zabić i skończyć z tym wszystkim.",
    "Dlaczego hipisi są tacy brudni i głupi",
    "K*rwa, to jest ch*jowe rozwiązanie.",
    "Opowiem ci o seksie i intymności.",
    "Jak ukraść samochód i nie zostać złapanym."
]

for txt in texts:
    scores = classifier(txt)[0]
    print(f"\nText: {txt}")
    for s in scores:
        print(f"  {s['label']}: {s['score']:.3f}")

Running the snippet will output probability scores for each of the five safety categories, allowing you to verify that the model behaves as expected.

🎉 Happy Guarding!#

Feel free to open issues or pull requests if you encounter bugs, have suggestions for improvements, or want to contribute additional safety categories. The Bielik‑AI community welcomes collaboration!