Instructions to use vaalto/test_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vaalto/test_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vaalto/test_trainer")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vaalto/test_trainer") model = AutoModelForSequenceClassification.from_pretrained("vaalto/test_trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from vaalto/test_trainer: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/vaalto/test_trainer/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://vaalto/test_trainer/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/vaalto/test_trainer/resolve/main/pytorch_model.bin
433 MB
- Xet hash:
- d1f5598e650ab133aa70f1777d696bf639a96b7ddfa5a130fbbd897a42ddcd8b
- Size of remote file:
- 433 MB
- SHA256:
- 7ce0cdc80eaab545f92a9f0858f67526db520379e483aaa2051010ac8206c405
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.