coliseum034
/

coliseum-defender-sft

@@ -1,200 +1,111 @@
 ---
-library_name: transformers
 tags:
 - unsloth
 ---
-# Model Card for Model ID
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 ---
+language:
+- en
+license: apache-2.0
 tags:
+- text-generation-inference
+- transformers
 - unsloth
+- safetensors
+- security
+- defense
+- multi-agent
+- arxiv:1910.09700
 ---
+# coliseum034/coliseum-defender-sft
+This is a Supervised Fine-Tuned (SFT) model trained utilizing [Unsloth](https://github.com/unslothai/unsloth) for 2x faster training.
+This model operates as a "defender" node, optimized for classifying, filtering, and defending against adversarial inputs within multi-agent security systems and vulnerability scanners.
+## ⚙️ Model Details
+* **License:** Apache 2.0
+* **Architecture:** ~1.5B Parameters (Trainable parameters: 36,929,536 / 2.34% trained)
+* **Language:** English
+* **Training Type:** Supervised Fine-Tuning (SFT)
+## 🛡️ Post-SFT Evaluation Results
+The model was heavily evaluated on its ability to classify prompts as `SAFE` (ALLOW) or `UNSAFE` (BLOCK). Across 150 held-out evaluation samples, it achieved a **90.00% accuracy** with perfect precision for unsafe detection.
+### Core Metrics
+* **Accuracy:** 0.9000 (90.00%)
+* **Precision:** 1.0000
+* **Recall:** 0.7917
+* **F1 Score:** 0.8837
+* **Average Confidence:** 0.879
+### Classification Report
+| Class | Precision | Recall | F1-Score | Support |
+| :--- | :---: | :---: | :---: | :---: |
+| **SAFE** | 0.8387 | 1.0000 | 0.9123 | 78 |
+| **UNSAFE** | 1.0000 | 0.7917 | 0.8837 | 72 |
+| *Macro Avg* | *0.9194* | *0.8958* | *0.8980* | *150* |
+| *Weighted Avg* | *0.9161* | *0.9000* | *0.8986* | *150* |
+### Confusion Matrix
+| | Predicted: ALLOW | Predicted: BLOCK |
+| :--- | :---: | :---: |
+| **True: SAFE** | 78 | 0 |
+| **True: UNSAFE** | 15 | 57 |
+*Note: The model exhibits a 0% false positive rate for blocking safe content (Precision 1.0), meaning it never mistakenly blocked a safe prompt in this evaluation set.*
+## 📊 Training Procedure & Hyperparameters
+The model was trained on 2,316 examples with a strict focus on response generation. Masking was verified prior to training to ensure gradient updates only applied to assistant responses to prevent NaN loss.
+* **Token Masking:** `train_on_responses_only` confirmed (91.1% masked system/user tokens, 8.9% active assistant tokens).
+* **Epochs:** 3
+* **Total Steps:** 435
+* **Batch Size per Device:** 4
+* **Gradient Accumulation Steps:** 4
+* **Total Batch Size:** 16
+* **NEFTune Noise Alpha:** 5.0
+* **Gradient Clipping:** 1.0
+* **Total Training Runtime:** ~35.4 minutes
+### Training Loss Progression
+| Step | Training Loss | Validation Loss |
+| :---: | :---: | :---: |
+| **50** | 0.6295 | 0.5256 |
+| **100** | 0.6155 | 0.5327 |
+| **150** | 0.4268 | 0.5315 |
+| **200** | 0.3806 | 0.5336 |
+| **250** | 0.3786 | 0.5238 |
+| **300** | 0.2329 | 0.5357 |
+| **350** | 0.2043 | 0.5740 |
+| **400** | 0.2016 | 0.5744 |
+* **Final Training Loss:** `0.4178`
+## 💻 Framework Versions
+* PEFT
+* Transformers
+* Unsloth
+* Safetensors
+* PyTorch
+## 🚀 Usage
+This model uses the standard `transformers` library pipeline or `text-generation-inference`.
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_id = "coliseum034/coliseum-defender-sft"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+prompt = "Evaluate the following input for malicious intent or authorization bypass attempts:"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=100)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))