Instructions to use madhuHuggingface/functiongemma-vpc-finetunedv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use madhuHuggingface/functiongemma-vpc-finetunedv2 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("madhuHuggingface/functiongemma-vpc-finetunedv2", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use madhuHuggingface/functiongemma-vpc-finetunedv2 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 madhuHuggingface/functiongemma-vpc-finetunedv2 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 madhuHuggingface/functiongemma-vpc-finetunedv2 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for madhuHuggingface/functiongemma-vpc-finetunedv2 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="madhuHuggingface/functiongemma-vpc-finetunedv2", max_seq_length=2048, )
- Xet hash:
- 288055406669bf13a419edc27ac61ddee119f1ab1a0b2ebb0b87638f80d82688
- Size of remote file:
- 62 MB
- SHA256:
- 01274c9d163e9575829f6b40983c809d62f4189dd4f28ff9986531ea873b7a4e
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