Instructions to use Muhammed164/SDPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Muhammed164/SDPO with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Muhammed164/SDPO", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio
How to use Muhammed164/SDPO 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 Muhammed164/SDPO 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 Muhammed164/SDPO to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Muhammed164/SDPO to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Muhammed164/SDPO", max_seq_length=2048, )
- Xet hash:
- ff78492fb6289121c2a723db02bc5e3469d1246af9cc1b47efc3567580c12420
- Size of remote file:
- 104 MB
- SHA256:
- cfdbeaba6f405ddd835994d4498a70666b22a02f42c00b97d4334a7f1e1e3bb2
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