Instructions to use google/bert_uncased_L-6_H-128_A-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/bert_uncased_L-6_H-128_A-2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("google/bert_uncased_L-6_H-128_A-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google/bert_uncased_L-6_H-128_A-2: direct link, hf CLI and curl.
- Browser
- Download file 20.7 MB
-
https://huggingface.co/google/bert_uncased_L-6_H-128_A-2/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://google/bert_uncased_L-6_H-128_A-2/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google/bert_uncased_L-6_H-128_A-2/resolve/main/flax_model.msgpack
20.7 MB
- Xet hash:
- 366803114ea0c96911c8b0c10216d4cddbe5fabf64bd5f888b080134c4b84c1d
- Size of remote file:
- 20.7 MB
- SHA256:
- 36f0f96413cb333904ca99c6f566a029b4b9d74cedf54e5e98e1772516f47e6b
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