Instructions to use tmnam20/gpt1_n-layer-2_xnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tmnam20/gpt1_n-layer-2_xnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tmnam20/gpt1_n-layer-2_xnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tmnam20/gpt1_n-layer-2_xnli") model = AutoModelForSequenceClassification.from_pretrained("tmnam20/gpt1_n-layer-2_xnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-37000/scheduler.pt from tmnam20/gpt1_n-layer-2_xnli: direct link, hf CLI and curl.
- Browser
- Download file 627 Bytes
-
https://huggingface.co/tmnam20/gpt1_n-layer-2_xnli/resolve/0db7b57c1fdb41ceeba8a6f90ef4a910a0a0e02d/checkpoint-37000/scheduler.pt
- Command line
-
hf download hf://tmnam20/gpt1_n-layer-2_xnli@0db7b57c1fdb41ceeba8a6f90ef4a910a0a0e02d/checkpoint-37000/scheduler.pt
-
curl -L -o scheduler.pt https://huggingface.co/tmnam20/gpt1_n-layer-2_xnli/resolve/0db7b57c1fdb41ceeba8a6f90ef4a910a0a0e02d/checkpoint-37000/scheduler.pt
627 Bytes
- Xet hash:
- b80349124e48704c6535f79c989cc5d535bf6e757a2bee257f699aac9cdc2a02
- Size of remote file:
- 627 Bytes
- SHA256:
- e5b9810532f52519df11091e5f13b3f90c377672bb4687ae2d4209a8b4ca5f8b
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