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vvvvtrt
/
RUNE-4M

Fill-Mask
Transformers
PyTorch
Russian
rune
russian
bert
encoder
efficient
lightweight
masked-lm
distillation
Model card Files Files and versions
xet
Community

Instructions to use vvvvtrt/RUNE-4M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use vvvvtrt/RUNE-4M with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("fill-mask", model="vvvvtrt/RUNE-4M")
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoModelForMaskedLM
    model = AutoModelForMaskedLM.from_pretrained("vvvvtrt/RUNE-4M", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
RUNE-4M
22.2 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 5 commits
vvvvtrt's picture
vvvvtrt
docs: set citation author Mikhail Chashin
48fc702 verified 7 days ago
  • assets
    Initial release: RUNE-4M Russian Ultra-efficient Encoder (4.25M) 7 days ago
  • charts
    Initial release: RUNE-4M Russian Ultra-efficient Encoder (4.25M) 7 days ago
  • .gitattributes
    1.52 kB
    initial commit 7 days ago
  • LICENSE
    11.4 kB
    Initial release: RUNE-4M Russian Ultra-efficient Encoder (4.25M) 7 days ago
  • README.md
    5.38 kB
    docs: set citation author Mikhail Chashin 7 days ago
  • config.json
    729 Bytes
    Initial release: RUNE-4M Russian Ultra-efficient Encoder (4.25M) 7 days ago
  • pytorch_model.bin
    17 MB
    xet
    Initial release: RUNE-4M Russian Ultra-efficient Encoder (4.25M) 7 days ago
  • tokenizer.json
    4.75 MB
    Initial release: RUNE-4M Russian Ultra-efficient Encoder (4.25M) 7 days ago
  • tokenizer_config.json
    406 Bytes
    Initial release: RUNE-4M Russian Ultra-efficient Encoder (4.25M) 7 days ago