secllmuser
/

constitutional-toxic-classifier-gemma

@@ -1,199 +1,185 @@
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 ---
+language: en
+license: apache-2.0
+base_model: google/gemma-2b
+tags:
+  - text-classification
+  - toxic-content
+  - safety
+  - constitutional-classifier
+  - lora
+  - peft
+  - gemma
+metrics:
+  - accuracy
+  - f1
+model-index:
+  - name: constitutional-toxic-classifier-gemma
+    results:
+      - task:
+          type: text-classification
+        metrics:
+          - type: accuracy
+            value: 0.8852
+          - type: f1
+            value: 0.9020
+          - type: precision
+            value: 0.8984
+          - type: recall
+            value: 0.9057
 ---
+# constitutional-toxic-classifier-gemma
+Constitutional toxic content classifier fine-tuned on synthetic safety data,
+inspired by Anthropic's [Constitutional Classifiers paper](https://arxiv.org/abs/2501.18837).
+**Type**: LoRA adapters only (tiny, ~10–30 MB). You need the base model `google/gemma-2b` and `peft` installed.
+---
+## Model Performance
+| Metric    | Value  |
+|-----------|--------|
+| Accuracy  | 0.8852 |
+| F1        | 0.9020 |
+| Precision | 0.8984 |
+| Recall    | 0.9057 |
+**Confusion matrix**
+|                | Predicted Safe | Predicted Toxic |
+|----------------|---------------|-----------------|
+| **Actual Safe**  | TN = 675     | FP = 113       |
+| **Actual Toxic** | FN = 104     | TP = 999       |
+---
+## Quick Start
+### Install
+```bash
+pip install transformers peft torch
+```
+> **Gemma license required** — accept the license at
+> <https://huggingface.co/google/gemma-2b> before downloading the base model.
+### Load and run inference
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from peft import PeftModel
+import torch
+BASE_MODEL = "google/gemma-2b"
+ADAPTER_REPO = "secllmuser/constitutional-toxic-classifier-gemma"
+# 1. Load base Gemma + LoRA adapters
+tokenizer = AutoTokenizer.from_pretrained(ADAPTER_REPO)
+base = AutoModelForSequenceClassification.from_pretrained(
+    BASE_MODEL,
+    num_labels=2,
+    torch_dtype=torch.float16,   # use float32 on CPU
+    trust_remote_code=True,
+)
+model = PeftModel.from_pretrained(base, ADAPTER_REPO)
+model.eval()
+# 2. Run inference
+text = "I will hurt you"
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)
+with torch.no_grad():
+    logits = model(**inputs).logits
+label_id = logits.argmax(-1).item()
+labels   = {0: "safe", 1: "toxic"}
+print(f"{text!r}  →  {labels[label_id]}")
+```
+### Batch inference
+```python
+texts = [
+    "Have a great day!",
+    "I will destroy you",
+    "Thanks for your help",
+    "You are worthless",
+]
+inputs = tokenizer(
+    texts,
+    return_tensors="pt",
+    padding=True,
+    truncation=True,
+    max_length=256,
+)
+with torch.no_grad():
+    logits = model(**inputs).logits
+labels = {0: "safe", 1: "toxic"}
+for text, pred in zip(texts, logits.argmax(-1).tolist()):
+    print(f"{labels[pred]:5s}  {text!r}")
+```
+---
 ## Training Details
+| Parameter      | Value          |
+|----------------|----------------|
+| Base model     | `google/gemma-2b` |
+| Task           | Binary sequence classification (safe / toxic) |
+| LoRA rank (r)  | 16       |
+| LoRA alpha     | 32   |
+| LoRA dropout   | 0.1 |
+| Target modules | q_proj, k_proj, v_proj, o_proj |
+| Max length     | 256   |
+| Learning rate  | 0.0002           |
+| Batch size     | 8   |
+| Training data  | Synthetic data generated from constitutional rules |
+---
+## Labels
+| ID | Label |
+|----|-------|
+| 0  | safe  |
+| 1  | toxic |
+---
+## Constitutional Approach
+The training data was generated using a **toxicity constitution** — a set of
+rules defining what counts as harmful content (hate speech, threats, harassment,
+self-harm promotion, etc.). Synthetic safe and toxic examples were generated
+from these rules to create balanced training data.
+See the original paper: [Constitutional Classifiers: Defending against Universal Jailbreaks](https://arxiv.org/abs/2501.18837)
+---
+## Limitations
+- Trained on synthetic data — real-world distribution may differ
+- English-only
+- Binary classification only (no severity scoring)
+- Context-blind: each text is classified independently
+---
+## Citation
+If you use this model, please cite:
+```bibtex
+@article{sharma2025constitutional,
+  title={Constitutional Classifiers: Defending against Universal Jailbreaks},
+  author={Sharma, Mrinank and others},
+  journal={arXiv preprint arXiv:2501.18837},
+  year={2025}
+}
+```