Instructions to use alphaedge-ai/mt5-base-mya-32768 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alphaedge-ai/mt5-base-mya-32768 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="alphaedge-ai/mt5-base-mya-32768")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("alphaedge-ai/mt5-base-mya-32768") model = AutoModelForSeq2SeqLM.from_pretrained("alphaedge-ai/mt5-base-mya-32768", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download spiece.model from alphaedge-ai/mt5-base-mya-32768: direct link, hf CLI and curl.
- Browser
- Download file 751 kB
-
https://huggingface.co/alphaedge-ai/mt5-base-mya-32768/resolve/main/spiece.model
- Command line
-
hf download hf://alphaedge-ai/mt5-base-mya-32768/spiece.model
-
curl -L -o spiece.model https://huggingface.co/alphaedge-ai/mt5-base-mya-32768/resolve/main/spiece.model
751 kB
- Xet hash:
- 0b6bdafe2b2ea91568b1942be8cd9e560a291d48f06f4c5bf216cd2197eede43
- Size of remote file:
- 751 kB
- SHA256:
- cf2dbab5a3ca593170314ed6889d4eebb012752395a9a0f193444e0f1db86cd8
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