Instructions to use hf-internal-testing/tiny-random-BertForPreTraining with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-BertForPreTraining with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertForPreTraining") model = AutoModelForPreTraining.from_pretrained("hf-internal-testing/tiny-random-BertForPreTraining", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from hf-internal-testing/tiny-random-BertForPreTraining: direct link, hf CLI and curl.
- Browser
- Download file 718 kB
-
https://huggingface.co/hf-internal-testing/tiny-random-BertForPreTraining/resolve/refs%2Fpr%2F1/tf_model.h5
- Command line
-
hf download hf://hf-internal-testing/tiny-random-BertForPreTraining@refs/pr/1/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/hf-internal-testing/tiny-random-BertForPreTraining/resolve/refs%2Fpr%2F1/tf_model.h5
718 kB
- Xet hash:
- 41c524fc114bba18300e829e91a93dd271e85216c658c2124a16522183dd263a
- Size of remote file:
- 718 kB
- SHA256:
- c2ccf4c24ae464ed3552e2ccb18ae1137e9037bc90dc13f7629395a03db1aeb9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.