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
- 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, )
Training in progress, step 1600
Browse files- README.md +1 -1
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README.md
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model_name: functiongemma-vpc-finetunedv2
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licence: license
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---
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model_name: functiongemma-vpc-finetunedv2
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tags:
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licence: license
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---
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adapter_config.json
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