Instructions to use RafatK/SMT-AZR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RafatK/SMT-AZR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="RafatK/SMT-AZR")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("RafatK/SMT-AZR") model = AutoModel.from_pretrained("RafatK/SMT-AZR", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from RafatK/SMT-AZR: direct link, hf CLI and curl.
- Browser
- Download file 31.2 MB
-
https://huggingface.co/RafatK/SMT-AZR/resolve/main/tokenizer.json
- Command line
-
hf download hf://RafatK/SMT-AZR/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/RafatK/SMT-AZR/resolve/main/tokenizer.json
31.2 MB
- Xet hash:
- a563bc73d47aeb2774355714aa93e95d3833ef63b59e77ea6d9d520a3d8c3360
- Size of remote file:
- 31.2 MB
- SHA256:
- bb08273b3ab8f38ebcb33558f96257ef97beeb0441f8a28eb886ae71d9f3e1fa
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