Instructions to use mohammadmahdinouri/albert-checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mohammadmahdinouri/albert-checkpoints with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="mohammadmahdinouri/albert-checkpoints")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("mohammadmahdinouri/albert-checkpoints") model = AutoModelForMaskedLM.from_pretrained("mohammadmahdinouri/albert-checkpoints", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from mohammadmahdinouri/albert-checkpoints: direct link, hf CLI and curl.
- Browser
- Download file 414 MB
-
https://huggingface.co/mohammadmahdinouri/albert-checkpoints/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://mohammadmahdinouri/albert-checkpoints/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mohammadmahdinouri/albert-checkpoints/resolve/main/pytorch_model.bin
414 MB
- Xet hash:
- 658d669c9963e9b9afecaa5197a80d0985184afc83e70e19a077c8cb0c32cae5
- Size of remote file:
- 414 MB
- SHA256:
- b95825212476b8ebdfe7daf4cc2a7995caf877efe746458798761b7bea2460f0
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