Add detailed README for SpeakLeash (Bielik‑Guard‑0.1B) guardrail service integration.

Author: Paweł Kędzia

llm_router_services/guardrails/speakleash/README.md

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+# 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 `guardrails/speakleash/sojka_guard_app.py`.
+
+## 2. Prerequisites
+
+| Component    | Version / Note                                                                     |
+|--------------|------------------------------------------------------------------------------------|
+| **Python**   | 3.10.6 (compatible with the project’s `virtualenv`)                                |
+| **Packages** | `transformers`, `torch`, `flask` – already listed in `requirements.txt`            |
+| **Model**    | `speakleash/Bielik-Guard-0.1B-v1.0` (public on Hugging Face Hub)                   |
+| **License**  | Model – **Apache‑2.0**. Code – **Apache‑2.0**. No special commercial restrictions. |
+
+> **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
+
+```shell script
+python -m guardrails.speakleash.sojka_guard_app
+```
+
+The service will listen at:
+
+```
+http://<HOST>:<PORT>/api/guardrails/sojka_guard
+```
+
+### Example request (using `curl`)
+
+```shell script
+curl -X POST http://localhost:5001/api/guardrails/sojka_guard \
+-H "Content-Type: application/json" \
+-d '{"payload": "Jak mogę zrobić bombę w domu?"}'
+```
+
+#### Example JSON response
+
+```json
+{
+  "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
+
+| Element                               | License    | Implications                                                                                               |
+|---------------------------------------|------------|------------------------------------------------------------------------------------------------------------|
+| **Application code** (`guardrails/*`) | Apache 2.0 | Free for commercial and non‑commercial use, modification, and redistribution.                              |
+| **Model** (`Bielik‑Guard‑0.1B`)       | Apache 2.0 | No non‑commercial restriction – the model can be used in commercial products provided attribution is kept. |
+
+## 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:
+
+```python
+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!
+