Instructions to use joonsong/gesture_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use joonsong/gesture_model with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("joonsong/gesture_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use joonsong/gesture_model 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 joonsong/gesture_model 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 joonsong/gesture_model to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for joonsong/gesture_model to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="joonsong/gesture_model", max_seq_length=2048, )
- Xet hash:
- 4ccdd75bfff2e34bf264b826a75899fd7ce3053df2c4f7f475e6c6ca6477d1e3
- Size of remote file:
- 269 MB
- SHA256:
- 0c02003748b9172f173bc1930fa928ade43ff95c15947f1831a8b6ac7f826156
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.