| --- |
| 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 |
| --- |
| |
| # 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. |
|
|
| This model is structurally geared toward advanced security operations, multi-agent system simulations, and red-teaming applications in the wild. |
|
|
| ## ⚙️ 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 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" |
| |
| tokenizer = AutoTokenizer.from_pretrained(model_id) |
| model = AutoModelForCausalLM.from_pretrained(model_id) |
| |
| prompt = "Analyze this sequence for potential exploitation vectors:" |
| inputs = tokenizer(prompt, return_tensors="pt") |
| |
| outputs = model.generate(**inputs, max_new_tokens=100) |
| print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
