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 training_args.bin from S2312dal/M3_MLM: direct link, hf CLI and curl.
- Browser
- Download file 3.31 kB
-
https://huggingface.co/S2312dal/M3_MLM/resolve/refs%2Fpr%2F2/training_args.bin
- Command line
-
hf download hf://S2312dal/M3_MLM@refs/pr/2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/S2312dal/M3_MLM/resolve/refs%2Fpr%2F2/training_args.bin
3.31 kB
- Xet hash:
- 5d8a93ff5443597cdd994740ec522fd9a6179ef21d3762924a9c601889124266
- Size of remote file:
- 3.31 kB
- SHA256:
- ae3404b51108596776235ea85ca35793525beb49c60dac5c14f20f237e85b441
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