Instructions to use nreimers/mMiniLMv2-L6-H384-distilled-from-XLMR-Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nreimers/mMiniLMv2-L6-H384-distilled-from-XLMR-Large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="nreimers/mMiniLMv2-L6-H384-distilled-from-XLMR-Large")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("nreimers/mMiniLMv2-L6-H384-distilled-from-XLMR-Large") model = AutoModelForMaskedLM.from_pretrained("nreimers/mMiniLMv2-L6-H384-distilled-from-XLMR-Large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c6df4f0269cb485b39638f845a7ec926422e5e7205fa4cfca16c57065a9664b8
- Size of remote file:
- 215 MB
- SHA256:
- 990349ad61517fae9ed88e820f0e60c7c09ef0e7b9d9b6bbde1b29b2af41088a
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