Instructions to use VJ24/gemma-7b-legal-risk-scorer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VJ24/gemma-7b-legal-risk-scorer with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("VJ24/gemma-7b-legal-risk-scorer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use VJ24/gemma-7b-legal-risk-scorer 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 VJ24/gemma-7b-legal-risk-scorer 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 VJ24/gemma-7b-legal-risk-scorer to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for VJ24/gemma-7b-legal-risk-scorer to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="VJ24/gemma-7b-legal-risk-scorer", max_seq_length=2048, )
- Xet hash:
- 1e33c32c21f7289d1b27e047cfb5862aaba60ff709e0d00664e74074eda42b45
- Size of remote file:
- 34.4 MB
- SHA256:
- 91600688617d8ac596b3681d04eae42dd11573018ab577755208fa81b3c6a529
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