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 flax_model.msgpack from pradhyra/AWSBlogBert: direct link, hf CLI and curl.
- Browser
- Download file 328 MB
-
https://huggingface.co/pradhyra/AWSBlogBert/resolve/refs%2Fpr%2F1/flax_model.msgpack
- Command line
-
hf download hf://pradhyra/AWSBlogBert@refs/pr/1/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/pradhyra/AWSBlogBert/resolve/refs%2Fpr%2F1/flax_model.msgpack
328 MB
- Xet hash:
- b2ae4bbd20ccc251248f00816c6aa9709f700498d3a570dd36e0530c0105d47e
- Size of remote file:
- 328 MB
- SHA256:
- 83043d5441a1c134e496f8d5f68b62fba8ce74b6c091ed1801c33237eba5abc3
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