coliseum-defender-sft

coliseum034/coliseum-defender-sft

This is a Supervised Fine-Tuned (SFT) model trained utilizing 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

Confusion Matrix

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

• 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.

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))