Instructions to use seele123/gemma-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use seele123/gemma-3 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("seele123/gemma-3", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use seele123/gemma-3 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 seele123/gemma-3 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 seele123/gemma-3 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for seele123/gemma-3 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="seele123/gemma-3", max_seq_length=2048, )
- Xet hash:
- c4c7aa84fb94a696d78f2bde62590a25b6741b7365ed842f9310b5dcab71d95d
- Size of remote file:
- 33.4 MB
- SHA256:
- 4667f2089529e8e7657cfb6d1c19910ae71ff5f28aa7ab2ff2763330affad795
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