Instructions to use JJ-Tae/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JJ-Tae/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="JJ-Tae/results")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("JJ-Tae/results") model = AutoModelForMaskedLM.from_pretrained("JJ-Tae/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from JJ-Tae/results: direct link, hf CLI and curl.
- Browser
- Download file 4.92 kB
-
https://huggingface.co/JJ-Tae/results/resolve/main/training_args.bin
- Command line
-
hf download hf://JJ-Tae/results/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/JJ-Tae/results/resolve/main/training_args.bin
4.92 kB
- Xet hash:
- 7b7c71dec5596c508d24dd47dbb23fc4066280e79d32ee8f7912d62dabad684b
- Size of remote file:
- 4.92 kB
- SHA256:
- c404bf296b7cd21c7f09e95d238b1a435e771398929ed82d3bec4bf55395bf9c
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