Instructions to use prajjwal1/bert-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prajjwal1/bert-tiny with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("prajjwal1/bert-tiny", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Add TF weights
Model converted by the [`transformers`' `pt_to_tf` CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). All converted model outputs and hidden layers were validated against its PyTorch counterpart. Maximum crossload output difference=2.766e-05; Maximum crossload hidden layer difference=3.219e-06; Maximum conversion output difference=2.766e-05; Maximum conversion hidden layer difference=3.219e-06;acf7178
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