Instructions to use spara/codegemma_tokenizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use spara/codegemma_tokenizer with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("spara/codegemma_tokenizer", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use spara/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 spara/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 spara/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 spara/codegemma_tokenizer to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="spara/codegemma_tokenizer", max_seq_length=2048, )
- Xet hash:
- b1a18734e478fdef9d2f506a6e5826b18be7a80760a7f2bc30b073d1aa7540ad
- Size of remote file:
- 17.5 MB
- SHA256:
- 3f8311e1140366e2d063d2f47f5af652e2cce5fdda518a7e5cb7d74524744f7f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.