Instructions to use S2312dal/M1_MLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use S2312dal/M1_MLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="S2312dal/M1_MLM")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("S2312dal/M1_MLM") model = AutoModelForMaskedLM.from_pretrained("S2312dal/M1_MLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from S2312dal/M1_MLM: direct link, hf CLI and curl.
- Browser
- Download file 44.9 MB
-
https://huggingface.co/S2312dal/M1_MLM/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://S2312dal/M1_MLM@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/S2312dal/M1_MLM/resolve/refs%2Fpr%2F1/pytorch_model.bin
44.9 MB
- Xet hash:
- f24c4da8e0de5e4d0735f19a9d3fb62c1907cbebeab0c7de3789fde5c45f7892
- Size of remote file:
- 44.9 MB
- SHA256:
- f3c587098d5a15a9a5cc146dfece551efc43b58824c0e80a1a69266d68886c06
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