Fill-Mask
Transformers
PyTorch
Russian
rune
russian
bert
encoder
efficient
lightweight
masked-lm
distillation
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
Commit History
28 days ago
-
docs: set citation author Mikhail Chashin
Verified signature48fc702 -
docs: remove naming meta table from card body
Verified signaturefc00046 -
docs: drop internal id and decoder LLM note
Verified signaturec2b24ab -
Initial release: RUNE-4M Russian Ultra-efficient Encoder (4.25M)
Verified signature0c5e387 -
initial commit
Verified signaturea26b1db