Instructions to use jxu124/TiO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jxu124/TiO with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="jxu124/TiO", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jxu124/TiO", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jxu124/TiO: direct link, hf CLI and curl.
- Browser
- Download file 4.39 GB
-
https://huggingface.co/jxu124/TiO/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jxu124/TiO/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jxu124/TiO/resolve/main/pytorch_model.bin
4.39 GB
- Xet hash:
- f68a775bbd339bbc4e7008ad287916d5403a0767d6108c63a3047c5e40f23442
- Size of remote file:
- 4.39 GB
- SHA256:
- 9521f3753abfe4f992a38139b2f6a794425f0a1f1a497ab3e23bb1423a58c0a1
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