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, )
metadata
base_model: unsloth/gemma-7b-it-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma
- trl
license: apache-2.0
language:
- en
Uploaded model
- Developed by: VJ24
- License: apache-2.0
- Finetuned from model : unsloth/gemma-7b-it-bnb-4bit
This gemma model was trained 2x faster with Unsloth
