Instructions to use SuperSecureHuman/trainer_test_checkpoint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SuperSecureHuman/trainer_test_checkpoint with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SuperSecureHuman/trainer_test_checkpoint")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SuperSecureHuman/trainer_test_checkpoint") model = AutoModelForSequenceClassification.from_pretrained("SuperSecureHuman/trainer_test_checkpoint", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-10/optimizer.pt from SuperSecureHuman/trainer_test_checkpoint: direct link, hf CLI and curl.
- Browser
- Download file 867 MB
-
https://huggingface.co/SuperSecureHuman/trainer_test_checkpoint/resolve/refs%2Fpr%2F1/checkpoint-10/optimizer.pt
- Command line
-
hf download hf://SuperSecureHuman/trainer_test_checkpoint@refs/pr/1/checkpoint-10/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/SuperSecureHuman/trainer_test_checkpoint/resolve/refs%2Fpr%2F1/checkpoint-10/optimizer.pt
867 MB
- Xet hash:
- 3cfa627c15ad480cde206abe26a9ce1ce7c3c1a17f866fe2257f411e8ee52f79
- Size of remote file:
- 867 MB
- SHA256:
- 5048ccd942c1fa8f84859abf13b3def087a58f67d403eb31ee5407850c2b73f0
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