Instructions to use nreimers/MiniLMv2-L6-H384-distilled-from-BERT-Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nreimers/MiniLMv2-L6-H384-distilled-from-BERT-Large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="nreimers/MiniLMv2-L6-H384-distilled-from-BERT-Large")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("nreimers/MiniLMv2-L6-H384-distilled-from-BERT-Large") model = AutoModelForMaskedLM.from_pretrained("nreimers/MiniLMv2-L6-H384-distilled-from-BERT-Large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from nreimers/MiniLMv2-L6-H384-distilled-from-BERT-Large: direct link, hf CLI and curl.
- Browser
- Download file 46.4 MB
-
https://huggingface.co/nreimers/MiniLMv2-L6-H384-distilled-from-BERT-Large/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://nreimers/MiniLMv2-L6-H384-distilled-from-BERT-Large/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/nreimers/MiniLMv2-L6-H384-distilled-from-BERT-Large/resolve/main/pytorch_model.bin
46.4 MB
- Xet hash:
- 0097c89e4e24ef19589d54e7faf660d34e8a5834940e720e8e98989e85852563
- Size of remote file:
- 46.4 MB
- SHA256:
- 3ee963a6fb88f3c83340899084e04c4556a8a618d41eebf3ffee068d5e98a9d6
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