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 training_args.bin from S2312dal/M6_MLM: direct link, hf CLI and curl.
- Browser
- Download file 3.31 kB
-
https://huggingface.co/S2312dal/M6_MLM/resolve/refs%2Fpr%2F2/training_args.bin
- Command line
-
hf download hf://S2312dal/M6_MLM@refs/pr/2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/S2312dal/M6_MLM/resolve/refs%2Fpr%2F2/training_args.bin
3.31 kB
- Xet hash:
- 21f47ffee3be9f40f0ebcfddcbfddb4e96777a52310989234d03b28a7b602e6a
- Size of remote file:
- 3.31 kB
- SHA256:
- 7cc85484164332b7a1b97f9d311420b3a86ab0caef8b53919717f1559d8d4f3c
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