Instructions to use MuazTPM/defender-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MuazTPM/defender-model with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MuazTPM/defender-model", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use MuazTPM/defender-model 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 MuazTPM/defender-model 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 MuazTPM/defender-model to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for MuazTPM/defender-model to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="MuazTPM/defender-model", max_seq_length=2048, )
- Xet hash:
- 9768e0203faedf7c582a9cb8115687b1a2396022e8d3a1ca939a83ae43ee4cab
- Size of remote file:
- 17.2 MB
- SHA256:
- a65c6c5f9764771aa485e6a1f5e63d7d9af8477fe0777148c17476ecb2e09a05
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