Instructions to use alphaedge-ai/mt5-base-kor-16384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alphaedge-ai/mt5-base-kor-16384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="alphaedge-ai/mt5-base-kor-16384")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("alphaedge-ai/mt5-base-kor-16384") model = AutoModelForSeq2SeqLM.from_pretrained("alphaedge-ai/mt5-base-kor-16384", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download spiece.model from alphaedge-ai/mt5-base-kor-16384: direct link, hf CLI and curl.
- Browser
- Download file 474 kB
-
https://huggingface.co/alphaedge-ai/mt5-base-kor-16384/resolve/main/spiece.model
- Command line
-
hf download hf://alphaedge-ai/mt5-base-kor-16384/spiece.model
-
curl -L -o spiece.model https://huggingface.co/alphaedge-ai/mt5-base-kor-16384/resolve/main/spiece.model
474 kB
- Xet hash:
- 0d5929110033c54a9310335415461a06c27623035c745d7894e0d100c9e7bc92
- Size of remote file:
- 474 kB
- SHA256:
- 4ccf83ea8f902184074e945c25ed7fa1d01c64c5b05f858d7f549d1e3abec663
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