Instructions to use google/bert_uncased_L-12_H-256_A-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/bert_uncased_L-12_H-256_A-4 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("google/bert_uncased_L-12_H-256_A-4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from google/bert_uncased_L-12_H-256_A-4: direct link, hf CLI and curl.
- Browser
- Download file 70.4 MB
-
https://huggingface.co/google/bert_uncased_L-12_H-256_A-4/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://google/bert_uncased_L-12_H-256_A-4/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/google/bert_uncased_L-12_H-256_A-4/resolve/main/pytorch_model.bin
70.4 MB
- Xet hash:
- 9de7dcdcd523b5a75219a6666fc9f64cae24d25ec636c2d7446600882378a17d
- Size of remote file:
- 70.4 MB
- SHA256:
- 1be1c8c6a57894a139fdf526fb8435f6acb6ea01600569a4ac3cfd405dc5f875
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