Instructions to use jingwang/foo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jingwang/foo with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jingwang/foo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use jingwang/foo 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 jingwang/foo 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 jingwang/foo to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for jingwang/foo to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="jingwang/foo", max_seq_length=2048, )
- Xet hash:
- fa39ea81c29d8c9ab9d209d2c75f799dfed3d1882ac4955dd33ded95909798c7
- Size of remote file:
- 168 MB
- SHA256:
- ff2f1b911abbe46ff3b5ce04cb0d3aa58b90ad043bd5e3cdb5c7981641d53e6c
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