---
base_model: unsloth/qwen2.5-0.5b-instruct-unsloth-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- safetensors
- security
- red-teaming
- adversarial-testing
---

# 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](https://github.com/unslothai/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`. 

```python
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))