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 model.safetensors from JJ-Tae/Pretraining_Test_v2: direct link, hf CLI and curl.
- Browser
- Download file 557 MB
-
https://huggingface.co/JJ-Tae/Pretraining_Test_v2/resolve/main/model.safetensors
- Command line
-
hf download hf://JJ-Tae/Pretraining_Test_v2/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/JJ-Tae/Pretraining_Test_v2/resolve/main/model.safetensors
557 MB
- Xet hash:
- 14670fbc83ca713c2b7a025327146f1e84faaeba170e804d9543cb161ca4b880
- Size of remote file:
- 557 MB
- SHA256:
- b53483fdc74eae5a6b341842c51aba39c1c751fb556d96f20c4789fb15817b3c
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