Instructions to use michisohn/gemma_human_values with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use michisohn/gemma_human_values with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("michisohn/gemma_human_values", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use michisohn/gemma_human_values 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 michisohn/gemma_human_values 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 michisohn/gemma_human_values to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for michisohn/gemma_human_values to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="michisohn/gemma_human_values", max_seq_length=2048, )
- Xet hash:
- 7b769d48d5bb0c11070733e46838f08f32d0a4eff117b6daa4c09da11160b33b
- Size of remote file:
- 200 MB
- SHA256:
- fb3c870810599a384810e6bb20cd0449fa42e54da67de768e260c649f1a6969c
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