Instructions to use S2312dal/M3_MLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use S2312dal/M3_MLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="S2312dal/M3_MLM")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("S2312dal/M3_MLM") model = AutoModelForMaskedLM.from_pretrained("S2312dal/M3_MLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from S2312dal/M3_MLM: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/S2312dal/M3_MLM/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://S2312dal/M3_MLM@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/S2312dal/M3_MLM/resolve/refs%2Fpr%2F1/pytorch_model.bin
433 MB
- Xet hash:
- 275b72836cc70d2f8997fc8f0e0ac75a78dd6b7a2e8cb6398d5859a6ddffa4f3
- Size of remote file:
- 433 MB
- SHA256:
- 9dc5689499fc73258ab5a19c425953f725f858799cb0b1fc1fbbc08a11f5a2b5
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