Instructions to use lucazed/LernnaviBERT_mcqbert3_correct_answers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lucazed/LernnaviBERT_mcqbert3_correct_answers with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, MCQBert3 tokenizer = AutoTokenizer.from_pretrained("lucazed/LernnaviBERT_mcqbert3_correct_answers") model = MCQBert3.from_pretrained("lucazed/LernnaviBERT_mcqbert3_correct_answers", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from lucazed/LernnaviBERT_mcqbert3_correct_answers: direct link, hf CLI and curl.
- Browser
- Download file 442 MB
-
https://huggingface.co/lucazed/LernnaviBERT_mcqbert3_correct_answers/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://lucazed/LernnaviBERT_mcqbert3_correct_answers/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/lucazed/LernnaviBERT_mcqbert3_correct_answers/resolve/main/pytorch_model.bin
442 MB
- Xet hash:
- 62844e10d8ec3938385c376da3fd5f01d0f17c306e73d8929e279bd9b2a0e6c0
- Size of remote file:
- 442 MB
- SHA256:
- 2f5fdffb3f16a9d31cfe312c6459ce7b5c909ee913b69e5a7f8ce44a3762f06b
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