Instructions to use S2312dal/M7_MLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use S2312dal/M7_MLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="S2312dal/M7_MLM")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("S2312dal/M7_MLM") model = AutoModelForMaskedLM.from_pretrained("S2312dal/M7_MLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from S2312dal/M7_MLM: direct link, hf CLI and curl.
- Browser
- Download file 329 MB
-
https://huggingface.co/S2312dal/M7_MLM/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://S2312dal/M7_MLM/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/S2312dal/M7_MLM/resolve/main/pytorch_model.bin
329 MB
- Xet hash:
- c22c6b1b09522ef881748a1b1b3ab9dcbb77c40dcb5ee3090993d7a1ec007477
- Size of remote file:
- 329 MB
- SHA256:
- 0457d9e8d316a398bf6cea50bcb463e29b4f50ff05a16cab19b1355b63016f34
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