Instructions to use alphaedge-ai/mt5-base-mar-16384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alphaedge-ai/mt5-base-mar-16384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="alphaedge-ai/mt5-base-mar-16384")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("alphaedge-ai/mt5-base-mar-16384") model = AutoModelForSeq2SeqLM.from_pretrained("alphaedge-ai/mt5-base-mar-16384", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download spiece.model from alphaedge-ai/mt5-base-mar-16384: direct link, hf CLI and curl.
- Browser
- Download file 496 kB
-
https://huggingface.co/alphaedge-ai/mt5-base-mar-16384/resolve/main/spiece.model
- Command line
-
hf download hf://alphaedge-ai/mt5-base-mar-16384/spiece.model
-
curl -L -o spiece.model https://huggingface.co/alphaedge-ai/mt5-base-mar-16384/resolve/main/spiece.model
496 kB
- Xet hash:
- 0112a8006e9f75823e62bf43df91d40d9ad52acc403253f12d0f961b2fa47b8a
- Size of remote file:
- 496 kB
- SHA256:
- 8446433876acda3eba026a168de58fce93a20305570e71b94b8a627ec62ed758
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