Transformers
Safetensors
English
text-generation-inference
unsloth
qwen2
trl
security
red-teaming
adversarial-testing
Instructions to use coliseum034/coliseum-attacker-dan with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use coliseum034/coliseum-attacker-dan with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("coliseum034/coliseum-attacker-dan", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use coliseum034/coliseum-attacker-dan with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for coliseum034/coliseum-attacker-dan to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for coliseum034/coliseum-attacker-dan to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for coliseum034/coliseum-attacker-dan to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="coliseum034/coliseum-attacker-dan", max_seq_length=2048, )
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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.
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## ⚙️ Model Details
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* **Developed by:** Vishva Patel (`vishva0`)
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* **License:** Apache 2.0
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* **Base Model:** `unsloth/qwen2.5-0.5b-instruct-unsloth-bnb-4bit`
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* **Architecture:** Qwen2 (0.5B parameters)
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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.
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This model is optimized for adversarial interactions, red-teaming, and generating edge-case scenarios for testing multi-agent security systems.
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## ⚙️ Model Details
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* **License:** Apache 2.0
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* **Base Model:** `unsloth/qwen2.5-0.5b-instruct-unsloth-bnb-4bit`
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* **Architecture:** Qwen2 (0.5B parameters)
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