Instructions to use Hebisuke/gemma-3-1b-it_lora16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hebisuke/gemma-3-1b-it_lora16 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Hebisuke/gemma-3-1b-it_lora16", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use Hebisuke/gemma-3-1b-it_lora16 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 Hebisuke/gemma-3-1b-it_lora16 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 Hebisuke/gemma-3-1b-it_lora16 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Hebisuke/gemma-3-1b-it_lora16 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Hebisuke/gemma-3-1b-it_lora16", max_seq_length=2048, )
- Xet hash:
- 9674bd731f122c645ef62bd099504a4fb24f7148fd1a69c2d291440976f4a0f3
- Size of remote file:
- 33.4 MB
- SHA256:
- f5d6e1d6078e96539fe2305f3b6843ea42fa5cb001dc6881474d6988e567ff92
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