Instructions to use alphaedge-ai/mt5-base-rus-16384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alphaedge-ai/mt5-base-rus-16384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="alphaedge-ai/mt5-base-rus-16384")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("alphaedge-ai/mt5-base-rus-16384") model = AutoModelForSeq2SeqLM.from_pretrained("alphaedge-ai/mt5-base-rus-16384", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download spiece.model from alphaedge-ai/mt5-base-rus-16384: direct link, hf CLI and curl.
- Browser
- Download file 546 kB
-
https://huggingface.co/alphaedge-ai/mt5-base-rus-16384/resolve/main/spiece.model
- Command line
-
hf download hf://alphaedge-ai/mt5-base-rus-16384/spiece.model
-
curl -L -o spiece.model https://huggingface.co/alphaedge-ai/mt5-base-rus-16384/resolve/main/spiece.model
546 kB
- Xet hash:
- 21c265019f8465977278a1d96967da1b80d219b5b964770ccf1c51aa34a7dfc2
- Size of remote file:
- 546 kB
- SHA256:
- cef71f4225da4832d3ce07d9859039506ce9c951d343fdcc1d9e3e8e022a19c3
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