coliseum034/coliseum-attacker-dan

This model is a fine-tuned version of unsloth/qwen2.5-0.5b-instruct-unsloth-bnb-4bit. It was trained up to 2x faster utilizing Unsloth and Hugging Face's TRL library.

This model is optimized for adversarial interactions, red-teaming, and generating edge-case scenarios for testing multi-agent security systems.

⚙️ Model Details

• License: Apache 2.0
• Base Model: unsloth/qwen2.5-0.5b-instruct-unsloth-bnb-4bit
• Architecture: Qwen2 (0.5B parameters)
• Language: English
• Quantization: 4-bit (bitsandbytes)

📊 Training & Evaluation Metrics

The model was trained over 4 epochs for a total of 276 global steps, with smart gradient offloading to optimize VRAM. The training procedure achieved a final validation perplexity of ~7.380.

Per-Epoch Results

Epoch	Training Loss	Validation Loss	Perplexity (PPL)
1.0	2.3769	2.2334	9.332
2.0	2.0010	2.0595	7.842
3.0	1.8116	1.9976	7.371
4.0	1.7036	1.9987	7.380

Final Held-Out Metrics

• Final Training Loss: 1.7036
• Final Evaluation Loss: 1.9987
• Final Perplexity: 7.380

Training Hyperparameters & Performance

• Global Steps: 276
• Total Training Runtime: ~26 minutes, 8 seconds (1568.302 seconds)
• Training Samples per Second: 2.778
• Training Steps per Second: 0.176
• Total FLOPs: 4.179 x 10^15

💻 Framework Versions

• PEFT
• Transformers
• Unsloth
• TRL
• Safetensors
• PyTorch

🚀 Usage

This model uses the standard transformers library pipeline or text-generation-inference.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "coliseum034/coliseum-attacker-dan"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

prompt = "Initiate testing parameters for potential authorization bypasses:"
inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))