Instructions to use alphaedge-ai/mt5-small-dan-16384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alphaedge-ai/mt5-small-dan-16384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="alphaedge-ai/mt5-small-dan-16384")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("alphaedge-ai/mt5-small-dan-16384") model = AutoModelForSeq2SeqLM.from_pretrained("alphaedge-ai/mt5-small-dan-16384", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download spiece.model from alphaedge-ai/mt5-small-dan-16384: direct link, hf CLI and curl.
- Browser
- Download file 494 kB
-
https://huggingface.co/alphaedge-ai/mt5-small-dan-16384/resolve/main/spiece.model
- Command line
-
hf download hf://alphaedge-ai/mt5-small-dan-16384/spiece.model
-
curl -L -o spiece.model https://huggingface.co/alphaedge-ai/mt5-small-dan-16384/resolve/main/spiece.model
494 kB
- Xet hash:
- d1ef5a837a61e4f15aad99fb8024793cb6e04b2200babf03620fca49b1879cee
- Size of remote file:
- 494 kB
- SHA256:
- 8420f2f5969ee4030c51701202084da54d4a0a01d3e0f24292e8f810d6eb402e
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