Instructions to use osanseviero/khipu_example with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use osanseviero/khipu_example with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="osanseviero/khipu_example")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("osanseviero/khipu_example") model = AutoModelForSequenceClassification.from_pretrained("osanseviero/khipu_example", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from osanseviero/khipu_example: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/osanseviero/khipu_example/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://osanseviero/khipu_example/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/osanseviero/khipu_example/resolve/main/pytorch_model.bin
433 MB
- Xet hash:
- 33a339c4eec70f3fe5a8bf306819f46e562e7786e3ad0d30f503feb561456c4a
- Size of remote file:
- 433 MB
- SHA256:
- d12833e7cee618acc729df7792bf37d323408e88e78cdbf0b2b6e0704538992c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.