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")# pip install -U transformers accelerate # 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-16000/optimizer.pt from tmnam20/gpt1_n-layer-2_xnli: direct link, hf CLI and curl.
- Browser
- Download file 365 MB
-
https://huggingface.co/tmnam20/gpt1_n-layer-2_xnli/resolve/4ac9cc9c04277e12ad92fba98ca22862b38adbdf/checkpoint-16000/optimizer.pt
- Command line
-
hf download hf://tmnam20/gpt1_n-layer-2_xnli@4ac9cc9c04277e12ad92fba98ca22862b38adbdf/checkpoint-16000/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/tmnam20/gpt1_n-layer-2_xnli/resolve/4ac9cc9c04277e12ad92fba98ca22862b38adbdf/checkpoint-16000/optimizer.pt
365 MB
- Xet hash:
- 561601cdc74f1a78939d45e39af115f915948954605d901b16f835cf016febe5
- Size of remote file:
- 365 MB
- SHA256:
- 6d3d6783963c967af3408fb11570567f49548d8577dc1170b413b18597d38c32
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