Instructions to use alphaedge-ai/mt5-small-srp-32768 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alphaedge-ai/mt5-small-srp-32768 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="alphaedge-ai/mt5-small-srp-32768")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("alphaedge-ai/mt5-small-srp-32768") model = AutoModelForSeq2SeqLM.from_pretrained("alphaedge-ai/mt5-small-srp-32768", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download spiece.model from alphaedge-ai/mt5-small-srp-32768: direct link, hf CLI and curl.
- Browser
- Download file 723 kB
-
https://huggingface.co/alphaedge-ai/mt5-small-srp-32768/resolve/main/spiece.model
- Command line
-
hf download hf://alphaedge-ai/mt5-small-srp-32768/spiece.model
-
curl -L -o spiece.model https://huggingface.co/alphaedge-ai/mt5-small-srp-32768/resolve/main/spiece.model
723 kB
- Xet hash:
- eae2e9ee013eaace65f5d38559f67bc44e479e0a2e77ced58d7190578d2429a0
- Size of remote file:
- 723 kB
- SHA256:
- 154bf6977d69cab9711057ff0bdf4ab5376baa7f2155f68bd0cffbfee6945367
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