Instructions to use qizunlee/gemma3n_E2B_it_ft_3RGarbageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use qizunlee/gemma3n_E2B_it_ft_3RGarbageClassification with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("qizunlee/gemma3n_E2B_it_ft_3RGarbageClassification", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use qizunlee/gemma3n_E2B_it_ft_3RGarbageClassification 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 qizunlee/gemma3n_E2B_it_ft_3RGarbageClassification 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 qizunlee/gemma3n_E2B_it_ft_3RGarbageClassification to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for qizunlee/gemma3n_E2B_it_ft_3RGarbageClassification to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="qizunlee/gemma3n_E2B_it_ft_3RGarbageClassification", max_seq_length=2048, )
- Xet hash:
- f2060c9004da07b778b2d954ee4de5a9a0c8767ebd64a337268deb9138ef45c4
- Size of remote file:
- 33.4 MB
- SHA256:
- 34e3f436416642ff2b4cba0602975a87bd8766c9f3b1851dde39479073bafd08
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