Instructions to use dvijkrish86/gemma-3-finetune-noice-final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dvijkrish86/gemma-3-finetune-noice-final with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("dvijkrish86/gemma-3-finetune-noice-final", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use dvijkrish86/gemma-3-finetune-noice-final 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 dvijkrish86/gemma-3-finetune-noice-final 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 dvijkrish86/gemma-3-finetune-noice-final to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for dvijkrish86/gemma-3-finetune-noice-final to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="dvijkrish86/gemma-3-finetune-noice-final", max_seq_length=2048, )
- Xet hash:
- 2d8dae08e10789b585c410aea24a439c395120ee085da16dd620ac57667b188c
- Size of remote file:
- 26.1 MB
- SHA256:
- 013b969315c45bf798704bf968ba41884de4c4a5b4d77b52408d0ac543309371
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