Instructions to use SMARTICT/gemma-3-4b-it-tr-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SMARTICT/gemma-3-4b-it-tr-ft with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SMARTICT/gemma-3-4b-it-tr-ft", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use SMARTICT/gemma-3-4b-it-tr-ft 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 SMARTICT/gemma-3-4b-it-tr-ft 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 SMARTICT/gemma-3-4b-it-tr-ft to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for SMARTICT/gemma-3-4b-it-tr-ft to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="SMARTICT/gemma-3-4b-it-tr-ft", max_seq_length=2048, )
- Xet hash:
- a81fa217b67ef4a1992b48a47651c27a2a19df419eafd1aad9c0bbd5ff49bde3
- Size of remote file:
- 4.69 MB
- SHA256:
- 1299c11d7cf632ef3b4e11937501358ada021bbdf7c47638d13c0ee982f2e79c
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