Instructions to use pradhyra/AWSBlogBert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pradhyra/AWSBlogBert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="pradhyra/AWSBlogBert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("pradhyra/AWSBlogBert") model = AutoModelForMaskedLM.from_pretrained("pradhyra/AWSBlogBert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from pradhyra/AWSBlogBert: direct link, hf CLI and curl.
- Browser
- Download file 330 MB
-
https://huggingface.co/pradhyra/AWSBlogBert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://pradhyra/AWSBlogBert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/pradhyra/AWSBlogBert/resolve/main/pytorch_model.bin
330 MB
- Xet hash:
- ec557fcb1d7075aab8cc74572f43ff13947ef839e472646f4a1ec7cb8df79636
- Size of remote file:
- 330 MB
- SHA256:
- dd1237679265b6593cb402d8990bc18c66d45d7c9bbdeac91cc6372c083f4b77
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.