Update README.md

README.md

@@ -1,22 +1,83 @@
 ---
 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
 ---
 
-# Uploaded  model
+# coliseum034/coliseum-attacker-wild
+
+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 structurally geared toward advanced security operations, multi-agent system simulations, and red-teaming applications in the wild.
+
+## ⚙️ 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 3 epochs for a total of 921 global steps. The training procedure demonstrated consistent learning, achieving a final validation perplexity of ~5.168.
+
+### Per-Epoch Results
+
+| Epoch | Training Loss | Validation Loss | Perplexity (PPL) |
+| :---: | :---: | :---: | :---: |
+| **1.0** | 1.6638 | 1.6605 | 5.262 |
+| **2.0** | 1.5345 | 1.6314 | 5.111 |
+| **3.0** | 1.4212 | 1.6425 | 5.168 |
+
+### Final Held-Out Metrics
+
+* **Final Training Loss:** `1.4212`
+* **Final Evaluation Loss:** `1.6425`
+* **Final Perplexity:** `5.168`
+
+### Training Hyperparameters & Performance
+
+* **Global Steps:** 921
+* **Total Training Runtime:** ~36 minutes, 48 seconds (2207.98 seconds)
+* **Training Samples per Second:** 6.658
+* **Training Steps per Second:** 0.417
+* **Total FLOPs:** 8.527 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-wild"
 
-- **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 = "Analyze this sequence for potential exploitation vectors:"
+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))