Instructions to use ALTAH/wojood_Tah with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ALTAH/wojood_Tah with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ALTAH/wojood_Tah")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ALTAH/wojood_Tah") model = AutoModelForMaskedLM.from_pretrained("ALTAH/wojood_Tah", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from ALTAH/wojood_Tah: direct link, hf CLI and curl.
- Browser
- Download file 8.39 MB
-
https://huggingface.co/ALTAH/wojood_Tah/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://ALTAH/wojood_Tah/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/ALTAH/wojood_Tah/resolve/main/flax_model.msgpack
8.39 MB
- Xet hash:
- 306e5c9690c243fe40d66034bee9bca9cb6a6a899ef053efa3e8009da7e623ee
- Size of remote file:
- 8.39 MB
- SHA256:
- 01954c0ecb1e0856bd1770fbbbfdf111765a4205e6125ceec341b62171b4c445
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