Instructions to use HmzBou/gemma-3-unsloth-4bit-lora32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HmzBou/gemma-3-unsloth-4bit-lora32 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("HmzBou/gemma-3-unsloth-4bit-lora32", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use HmzBou/gemma-3-unsloth-4bit-lora32 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 HmzBou/gemma-3-unsloth-4bit-lora32 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 HmzBou/gemma-3-unsloth-4bit-lora32 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for HmzBou/gemma-3-unsloth-4bit-lora32 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="HmzBou/gemma-3-unsloth-4bit-lora32", max_seq_length=2048, )
- Xet hash:
- 5d3dee64bab5a08f235cb14e6e099f23a413bc03ff7ca8dee3a7b2123b224f8d
- Size of remote file:
- 104 MB
- SHA256:
- 0ecebcb72fa53e4f1f724cd87b18b7a95e70f5fde7722a024b6c679eaa3cb811
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