Instructions to use S2312dal/M6_MLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use S2312dal/M6_MLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="S2312dal/M6_MLM")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("S2312dal/M6_MLM") model = AutoModelForMaskedLM.from_pretrained("S2312dal/M6_MLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from S2312dal/M6_MLM: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/S2312dal/M6_MLM/resolve/refs%2Fpr%2F2/pytorch_model.bin
- Command line
-
hf download hf://S2312dal/M6_MLM@refs/pr/2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/S2312dal/M6_MLM/resolve/refs%2Fpr%2F2/pytorch_model.bin
438 MB
- Xet hash:
- fd306991571f7d5e32cca839ba3aeb70ae3eb7c705364307ce8845cb3820e8b7
- Size of remote file:
- 438 MB
- SHA256:
- 4fb07fd9e54b6acf961b878043f5a8309695d577a953c83652e71e16a10e9356
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