Instructions to use omkar334/codegemma_tokenizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use omkar334/codegemma_tokenizer with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("omkar334/codegemma_tokenizer", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use omkar334/codegemma_tokenizer 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 omkar334/codegemma_tokenizer 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 omkar334/codegemma_tokenizer to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for omkar334/codegemma_tokenizer to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="omkar334/codegemma_tokenizer", max_seq_length=2048, )
- Xet hash:
- fde8653f2f656fb4ab30c2a5db64ba86a916a86134355b76c3bf26a5b022b323
- Size of remote file:
- 4.24 MB
- SHA256:
- 61a7b147390c64585d6c3543dd6fc636906c9af3865a5548f27f31aee1d4c8e2
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