Instructions to use pelican/test_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pelican/test_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pelican/test_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pelican/test_model") model = AutoModelForSequenceClassification.from_pretrained("pelican/test_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from pelican/test_model: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/pelican/test_model/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://pelican/test_model@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/pelican/test_model/resolve/refs%2Fpr%2F1/pytorch_model.bin
499 MB
- Xet hash:
- a8283786bf3351c485bf552657f53149371d3ac4509f3d120101687a37133605
- Size of remote file:
- 499 MB
- SHA256:
- ccffdcf7189776734729f40c2c6ba3270d07d5ce9b2349f32a35023cdf9bf68e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.