Instructions to use S2312dal/M7_MLM_final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use S2312dal/M7_MLM_final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="S2312dal/M7_MLM_final")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("S2312dal/M7_MLM_final") model = AutoModelForMaskedLM.from_pretrained("S2312dal/M7_MLM_final", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from S2312dal/M7_MLM_final: direct link, hf CLI and curl.
- Browser
- Download file 329 MB
-
https://huggingface.co/S2312dal/M7_MLM_final/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://S2312dal/M7_MLM_final/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/S2312dal/M7_MLM_final/resolve/main/pytorch_model.bin
329 MB
- Xet hash:
- 8c1fa5c58da56708a746a2d61dbe9a2590c5f665f2d1677da037e2797703dda2
- Size of remote file:
- 329 MB
- SHA256:
- 7c40f38e9763815816a502ac3f26299fbf4178fc94567a435d208448165a2a4f
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