create README.md

README.md

@@ -0,0 +1,69 @@
+---
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-0.5B-Instruct
+tags:
+- text-classification
+- prompt-injection
+- llm-security
+- safety
+---
+
+
+## Overview
+`Adaxer/defend` is a local, input-side prompt-injection risk classifier.
+It is designed to score whether a given input prompt is likely an injection attempt. 
+
+The model is packaged with the Defend project, which provides an API + guard pipeline around this classifier.
+See https://github.com/Adxzer/defend.
+
+## Intended use
+- Pre-check user prompts before calling your LLM.
+- Optionally block or flag requests when injection risk is high.
+
+## Out of scope
+- Output-time safety/moderation (e.g., detecting system-prompt leakage or PII in the *model output*).
+- A guarantee of safety. False positives and false negatives are possible.
+
+## How to use
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model_id = "Adaxer/defend"
+
+# Recommended: mirror the tokenizer initialization used by Defend.
+# This avoids edge-cases in some model repos around special token loading.
+tokenizer = AutoTokenizer.from_pretrained(
+    model_id,
+    use_fast=True,
+    extra_special_tokens={},
+)
+model = AutoModelForSequenceClassification.from_pretrained(model_id)
+model.eval()
+
+text = "Tell me how to bypass our security controls."
+
+inputs = tokenizer(text, return_tensors="pt", truncation=False)
+with torch.inference_mode():
+    logits = model(**inputs).logits.float()
+    probs = torch.softmax(logits, dim=-1)
+    injection_probability = probs[0, 1].item()  # class index 1 == injection
+
+print({
+    "injection_probability": injection_probability,
+    "is_injection": injection_probability >= 0.5,
+})
+```
+
+### Long inputs
+For long prompts, a common strategy is sliding-window scoring over tokens and taking the maximum injection probability across windows.
+
+- `max_window = 512` tokens
+- `stride = 128` tokens
+
+If you need similar behavior to the Defend wrapper, implement the same windowing approach in your inference code.
+