Instructions to use JJ-Tae/Pretraining_Test_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JJ-Tae/Pretraining_Test_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="JJ-Tae/Pretraining_Test_v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("JJ-Tae/Pretraining_Test_v2") model = AutoModelForMaskedLM.from_pretrained("JJ-Tae/Pretraining_Test_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from JJ-Tae/Pretraining_Test_v2: direct link, hf CLI and curl.
- Browser
- Download file 4.92 kB
-
https://huggingface.co/JJ-Tae/Pretraining_Test_v2/resolve/main/training_args.bin
- Command line
-
hf download hf://JJ-Tae/Pretraining_Test_v2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/JJ-Tae/Pretraining_Test_v2/resolve/main/training_args.bin
4.92 kB
- Xet hash:
- d12630b338c6303800aa401fc9e1eea77bfae9c796f40e22077ef4d5fb3f3862
- Size of remote file:
- 4.92 kB
- SHA256:
- 108f373d68f0a489000f8ffad4e0e5a04b58d939955eeb3c0350e8dc915e41ba
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