Instructions to use alphaedge-ai/mt5-small-arb-32768 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alphaedge-ai/mt5-small-arb-32768 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="alphaedge-ai/mt5-small-arb-32768")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("alphaedge-ai/mt5-small-arb-32768") model = AutoModelForSeq2SeqLM.from_pretrained("alphaedge-ai/mt5-small-arb-32768", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download spiece.model from alphaedge-ai/mt5-small-arb-32768: direct link, hf CLI and curl.
- Browser
- Download file 723 kB
-
https://huggingface.co/alphaedge-ai/mt5-small-arb-32768/resolve/main/spiece.model
- Command line
-
hf download hf://alphaedge-ai/mt5-small-arb-32768/spiece.model
-
curl -L -o spiece.model https://huggingface.co/alphaedge-ai/mt5-small-arb-32768/resolve/main/spiece.model
723 kB
- Xet hash:
- 4d0d18fba7e9e253f17d6e347c6203d439c4a8ef5ae43324828a82c50fbf7b9b
- Size of remote file:
- 723 kB
- SHA256:
- 70bbd4e4b0071bca89ae072d89f9a529d7237295c3136d72b8768940f889396d
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