Instructions to use shanjivkr/vit-gemma-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shanjivkr/vit-gemma-model with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("shanjivkr/vit-gemma-model", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use shanjivkr/vit-gemma-model 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 shanjivkr/vit-gemma-model 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 shanjivkr/vit-gemma-model to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for shanjivkr/vit-gemma-model to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="shanjivkr/vit-gemma-model", max_seq_length=2048, )
- Xet hash:
- 74dc450cfff2b9064b7137a30dadf1ed22fecef9becd4b0a232e2ec7ebaa7b4c
- Size of remote file:
- 34.4 MB
- SHA256:
- a732c83d0226629c6377889db202ef41fb7ba46b47bcf635c5dc260a09a930c1
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