Update README.md

README.md

@@ -1,22 +1,85 @@
 ---
 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
-license: apache-2.0
-language:
-- en
+- safetensors
+- security
+- red-teaming
+- adversarial-testing
 ---
 
-# Uploaded  model
+# 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. 
+
+Developed by Vishva Patel (`vishva0`), this model is optimized for adversarial interactions, red-teaming, and generating edge-case scenarios for testing multi-agent security systems.
+
+## ⚙️ Model Details
+
+* **Developed by:** Vishva Patel (`vishva0`)
+* **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"
 
-- **Developed by:** vishva0
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/qwen2.5-0.5b-instruct-unsloth-bnb-4bit
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
 
-This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)
+prompt = "Initiate testing parameters for potential authorization bypasses:"
+inputs = tokenizer(prompt, return_tensors="pt")
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+outputs = model.generate(**inputs, max_new_tokens=100)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))