Instructions to use fgaim/tibert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fgaim/tibert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="fgaim/tibert-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("fgaim/tibert-base") model = AutoModelForMaskedLM.from_pretrained("fgaim/tibert-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from fgaim/tibert-base: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/fgaim/tibert-base/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://fgaim/tibert-base/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/fgaim/tibert-base/resolve/main/flax_model.msgpack
438 MB
- Xet hash:
- c1b5b657fa5f4f453ec1ecc48ff284c02695ad2d295590ce9ff61ba938171586
- Size of remote file:
- 438 MB
- SHA256:
- 3711b1e43d645c1d2bb421e869f0c3a9394a9c9a0a360da494cda744ef51a54d
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