Instructions to use Jack-Payne1/EM_TEST with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jack-Payne1/EM_TEST with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Jack-Payne1/EM_TEST", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use Jack-Payne1/EM_TEST 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 Jack-Payne1/EM_TEST 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 Jack-Payne1/EM_TEST to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Jack-Payne1/EM_TEST to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Jack-Payne1/EM_TEST", max_seq_length=2048, )
- Xet hash:
- 47bef86a022875049e129bc56a34227f6f0f020a5fcc7845792d28d608cf761f
- Size of remote file:
- 5.97 kB
- SHA256:
- 591380c2089627b1aa728bfd52abdb50afeaafc7a7f48353dede4e443fc370d0
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