Instructions to use S2312dal/M2_MLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use S2312dal/M2_MLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="S2312dal/M2_MLM")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("S2312dal/M2_MLM") model = AutoModelForMaskedLM.from_pretrained("S2312dal/M2_MLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from S2312dal/M2_MLM: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/S2312dal/M2_MLM/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://S2312dal/M2_MLM/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/S2312dal/M2_MLM/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- b935f3d129f5bd382059b77830e05eb0f791895a1793b61537b748264a1cc1dd
- Size of remote file:
- 499 MB
- SHA256:
- 4b479032c29ff7f0658c14745def4c9ef9aba36bd84cbde76adbf08897f1ccc8
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