Instructions to use mohammadmahdinouri/moa-adapter-checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mohammadmahdinouri/moa-adapter-checkpoints with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="mohammadmahdinouri/moa-adapter-checkpoints")# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("mohammadmahdinouri/moa-adapter-checkpoints", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from mohammadmahdinouri/moa-adapter-checkpoints: direct link, hf CLI and curl.
- Browser
- Download file 122 MB
-
https://huggingface.co/mohammadmahdinouri/moa-adapter-checkpoints/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://mohammadmahdinouri/moa-adapter-checkpoints/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mohammadmahdinouri/moa-adapter-checkpoints/resolve/main/pytorch_model.bin
122 MB
- Xet hash:
- 437324b4dac3d7f3fa3fff2b6d2e078a877f6cecfcb5bc2756631f708927becd
- Size of remote file:
- 122 MB
- SHA256:
- 29497b1f2e29a532ebc7af7e245d90f14614d760c2c110047e6b4fee4a05452a
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