Instructions to use radned/test-trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use radned/test-trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="radned/test-trainer")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("radned/test-trainer") model = AutoModelForSequenceClassification.from_pretrained("radned/test-trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from radned/test-trainer: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/radned/test-trainer/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://radned/test-trainer@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/radned/test-trainer/resolve/refs%2Fpr%2F1/pytorch_model.bin
438 MB
- Xet hash:
- 1ed764a4254365abdbfa665dd4e9dd9d9fece8989e0e52554aa8c3bf06ee8796
- Size of remote file:
- 438 MB
- SHA256:
- 3f20e2ccbe36d191a16bbe23bbe67abdb1d1884dbb9610aed3904cb3ac0335c5
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