Instructions to use ProceduralTree/HW3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProceduralTree/HW3 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMultipleChoice tokenizer = AutoTokenizer.from_pretrained("ProceduralTree/HW3") model = AutoModelForMultipleChoice.from_pretrained("ProceduralTree/HW3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ProceduralTree/HW3: direct link, hf CLI and curl.
- Browser
- Download file 541 MB
-
https://huggingface.co/ProceduralTree/HW3/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://ProceduralTree/HW3@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ProceduralTree/HW3/resolve/refs%2Fpr%2F1/pytorch_model.bin
541 MB
- Xet hash:
- 3bc02227e97923bffcbf17db68ddc03641111ff9de5340ca8f398a2e29b83438
- Size of remote file:
- 541 MB
- SHA256:
- d9252f1e3e71a120d15edc193fb1f1942f1c4afdf0ad9b8129357ba0a1878643
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.